{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ff597aa",
   "metadata": {},
   "source": [
    "# 8 - Os peixes-bois condicionais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "204f83ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import networkx as nx\n",
    "# from causalgraphicalmodels import CausalGraphicalModel\n",
    "\n",
    "import arviz as az\n",
    "# ArviZ ships with style sheets!\n",
    "# https://python.arviz.org/en/stable/examples/styles.html#example-styles\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "\n",
    "import xarray as xr\n",
    "\n",
    "import stan\n",
    "import nest_asyncio\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.rcParams['axes.facecolor'] = 'lightgray'\n",
    "\n",
    "# To DAG's\n",
    "import daft\n",
    "from causalgraphicalmodels import CausalGraphicalModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a824bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add fonts to matplotlib to run xkcd\n",
    "\n",
    "from matplotlib import font_manager\n",
    "\n",
    "font_dirs = [\"fonts/\"]  # The path to the custom font file.\n",
    "font_files = font_manager.findSystemFonts(fontpaths=font_dirs)\n",
    "\n",
    "for font_file in font_files:\n",
    "    font_manager.fontManager.addfont(font_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c98a39a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.xkcd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d769e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To running the stan in jupyter notebook\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcc96c9",
   "metadata": {},
   "source": [
    "### R Code 8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fadc80f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isocode</th>\n",
       "      <th>isonum</th>\n",
       "      <th>country</th>\n",
       "      <th>rugged</th>\n",
       "      <th>rugged_popw</th>\n",
       "      <th>rugged_slope</th>\n",
       "      <th>rugged_lsd</th>\n",
       "      <th>rugged_pc</th>\n",
       "      <th>land_area</th>\n",
       "      <th>lat</th>\n",
       "      <th>...</th>\n",
       "      <th>africa_region_w</th>\n",
       "      <th>africa_region_e</th>\n",
       "      <th>africa_region_c</th>\n",
       "      <th>slave_exports</th>\n",
       "      <th>dist_slavemkt_atlantic</th>\n",
       "      <th>dist_slavemkt_indian</th>\n",
       "      <th>dist_slavemkt_saharan</th>\n",
       "      <th>dist_slavemkt_redsea</th>\n",
       "      <th>pop_1400</th>\n",
       "      <th>european_descent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>533</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.380</td>\n",
       "      <td>1.226</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.000</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.508</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>614.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2.518</td>\n",
       "      <td>1.469</td>\n",
       "      <td>7.414</td>\n",
       "      <td>0.720</td>\n",
       "      <td>39.004</td>\n",
       "      <td>65209.0</td>\n",
       "      <td>33.833</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1870829.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGO</td>\n",
       "      <td>24</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.714</td>\n",
       "      <td>2.274</td>\n",
       "      <td>0.228</td>\n",
       "      <td>4.906</td>\n",
       "      <td>124670.0</td>\n",
       "      <td>-12.299</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3610000.0</td>\n",
       "      <td>5.669</td>\n",
       "      <td>6.981</td>\n",
       "      <td>4.926</td>\n",
       "      <td>3.872</td>\n",
       "      <td>1223208.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AIA</td>\n",
       "      <td>660</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.231</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>8</td>\n",
       "      <td>Albania</td>\n",
       "      <td>3.427</td>\n",
       "      <td>1.597</td>\n",
       "      <td>10.451</td>\n",
       "      <td>1.006</td>\n",
       "      <td>62.133</td>\n",
       "      <td>2740.0</td>\n",
       "      <td>41.143</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  isocode  isonum      country  rugged  rugged_popw  rugged_slope  rugged_lsd  \\\n",
       "0     ABW     533        Aruba   0.462        0.380         1.226       0.144   \n",
       "1     AFG       4  Afghanistan   2.518        1.469         7.414       0.720   \n",
       "2     AGO      24       Angola   0.858        0.714         2.274       0.228   \n",
       "3     AIA     660     Anguilla   0.013        0.010         0.026       0.006   \n",
       "4     ALB       8      Albania   3.427        1.597        10.451       1.006   \n",
       "\n",
       "   rugged_pc  land_area     lat  ...  africa_region_w  africa_region_e  \\\n",
       "0      0.000       18.0  12.508  ...                0                0   \n",
       "1     39.004    65209.0  33.833  ...                0                0   \n",
       "2      4.906   124670.0 -12.299  ...                0                0   \n",
       "3      0.000        9.0  18.231  ...                0                0   \n",
       "4     62.133     2740.0  41.143  ...                0                0   \n",
       "\n",
       "   africa_region_c  slave_exports  dist_slavemkt_atlantic  \\\n",
       "0                0            0.0                     NaN   \n",
       "1                0            0.0                     NaN   \n",
       "2                1      3610000.0                   5.669   \n",
       "3                0            0.0                     NaN   \n",
       "4                0            0.0                     NaN   \n",
       "\n",
       "   dist_slavemkt_indian  dist_slavemkt_saharan  dist_slavemkt_redsea  \\\n",
       "0                   NaN                    NaN                   NaN   \n",
       "1                   NaN                    NaN                   NaN   \n",
       "2                 6.981                  4.926                 3.872   \n",
       "3                   NaN                    NaN                   NaN   \n",
       "4                   NaN                    NaN                   NaN   \n",
       "\n",
       "    pop_1400  european_descent  \n",
       "0      614.0               NaN  \n",
       "1  1870829.0               0.0  \n",
       "2  1223208.0               2.0  \n",
       "3        NaN               NaN  \n",
       "4   200000.0             100.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/rugged.csv', sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "259a322e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rgdppc_2000</th>\n",
       "      <th>log_gdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1794.729</td>\n",
       "      <td>7.492609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3703.113</td>\n",
       "      <td>8.216929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rgdppc_2000   log_gdp\n",
       "0          NaN       NaN\n",
       "1          NaN       NaN\n",
       "2     1794.729  7.492609\n",
       "3          NaN       NaN\n",
       "4     3703.113  8.216929"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['log_gdp'] = np.log(df['rgdppc_2000'])  # Log version of outcome\n",
    "df[['rgdppc_2000', 'log_gdp']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d45cdaa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_gdp_std</th>\n",
       "      <th>rugged_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.879712</td>\n",
       "      <td>0.138342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.964755</td>\n",
       "      <td>0.552564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.166270</td>\n",
       "      <td>0.123992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.104485</td>\n",
       "      <td>0.124960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.914904</td>\n",
       "      <td>0.433409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.996681</td>\n",
       "      <td>0.270397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0.783032</td>\n",
       "      <td>0.374557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>1.074365</td>\n",
       "      <td>0.283941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>0.780967</td>\n",
       "      <td>0.085940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>0.918589</td>\n",
       "      <td>0.192519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     log_gdp_std  rugged_std\n",
       "2       0.879712    0.138342\n",
       "4       0.964755    0.552564\n",
       "7       1.166270    0.123992\n",
       "8       1.104485    0.124960\n",
       "9       0.914904    0.433409\n",
       "..           ...         ...\n",
       "229     0.996681    0.270397\n",
       "230     0.783032    0.374557\n",
       "231     1.074365    0.283941\n",
       "232     0.780967    0.085940\n",
       "233     0.918589    0.192519\n",
       "\n",
       "[170 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf = df[~np.isnan(df['log_gdp'].values)].copy()\n",
    "\n",
    "ddf['log_gdp_std'] = ddf['log_gdp'] / np.mean(ddf['log_gdp'])\n",
    "ddf['rugged_std'] = ddf['rugged'] / np.max(ddf['rugged'])\n",
    "\n",
    "ddf[['log_gdp_std', 'rugged_std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91aac337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 8))\n",
    "\n",
    "plt.hist(ddf['log_gdp_std'], density=True, rwidth=0.9)\n",
    "plt.title('log_gdp \\n 1: mean; \\n 0.8: 80% of the average \\n 1.1: 10% more than average')\n",
    "plt.axvline(x=1, c='r', ls='--')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(17, 8))\n",
    "plt.hist(ddf['rugged_std'], density=True, rwidth=0.9)\n",
    "plt.title('Rugged \\n min:0 and max:1')\n",
    "plt.axvline(x=np.mean(ddf['rugged_std']), c='r', ls='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c7baf5",
   "metadata": {},
   "source": [
    "### R Code 8.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01ab89de",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 5.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3.5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_qk2y2370/model_ujreyjxj.stan', line 29, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 6.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 7.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] log_gdp_std;\n",
    "        vector[N] rugged_std;\n",
    "        real rugged_std_average;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta;\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta * (rugged_std - rugged_std_average);\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        \n",
    "        alpha ~ normal(1, 1);\n",
    "        beta ~ normal(0, 1);\n",
    "        sigma ~ exponential(1);\n",
    "        \n",
    "        // Likelihood\n",
    "        log_gdp_std ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;  // By default, if a variable log_lik is present in the Stan model, it will be retrieved as pointwise log likelihood values.\n",
    "        vector[N] log_gdp_std_hat;\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
    "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(ddf),\n",
    "    'log_gdp_std': ddf['log_gdp_std'].values,\n",
    "    'rugged_std': ddf['rugged_std'].values,\n",
    "    'rugged_std_average': ddf['rugged_std'].mean(),\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model, data=data)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9bd4387",
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       "      <th>parameters</th>\n",
       "      <th>lp__</th>\n",
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       "      <td>0.082170</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-249.604298</td>\n",
       "      <td>0.997929</td>\n",
       "      <td>-0.054581</td>\n",
       "      <td>0.128852</td>\n",
       "      <td>...</td>\n",
       "      <td>1.191371</td>\n",
       "      <td>1.000939</td>\n",
       "      <td>0.896899</td>\n",
       "      <td>0.941724</td>\n",
       "      <td>0.966914</td>\n",
       "      <td>1.054525</td>\n",
       "      <td>0.813120</td>\n",
       "      <td>0.728859</td>\n",
       "      <td>1.209415</td>\n",
       "      <td>1.016214</td>\n",
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       "      <td>0.013175</td>\n",
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       "parameters        lp__  accept_stat__  stepsize__  treedepth__  n_leapfrog__  \\\n",
       "draws                                                                          \n",
       "0           250.223848       1.000000    0.783507          2.0           3.0   \n",
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       "2           250.407073       1.000000    0.701591          2.0           3.0   \n",
       "3           249.859962       0.868779    0.773159          2.0           3.0   \n",
       "4           250.238049       0.955552    0.783507          3.0           7.0   \n",
       "\n",
       "parameters  divergent__    energy__     alpha      beta     sigma  ...  \\\n",
       "draws                                                              ...   \n",
       "0                   0.0 -248.561315  1.004732  0.082170  0.137878  ...   \n",
       "1                   0.0 -249.604298  0.997929 -0.054581  0.128852  ...   \n",
       "2                   0.0 -248.170058  1.011967  0.013175  0.143171  ...   \n",
       "3                   0.0 -249.776934  0.990636  0.011701  0.148956  ...   \n",
       "4                   0.0 -248.995941  0.999829 -0.081176  0.138578  ...   \n",
       "\n",
       "parameters  log_gdp_std_hat.161  log_gdp_std_hat.162  log_gdp_std_hat.163  \\\n",
       "draws                                                                       \n",
       "0                      1.118976             1.119565             0.740072   \n",
       "1                      1.191371             1.000939             0.896899   \n",
       "2                      1.164594             1.138025             1.067104   \n",
       "3                      1.026118             1.070148             1.096556   \n",
       "4                      1.019760             1.268163             0.770706   \n",
       "\n",
       "parameters  log_gdp_std_hat.164  log_gdp_std_hat.165  log_gdp_std_hat.166  \\\n",
       "draws                                                                       \n",
       "0                      1.142818             1.063478             0.814552   \n",
       "1                      0.941724             0.966914             1.054525   \n",
       "2                      0.911622             0.865265             1.124657   \n",
       "3                      0.929882             0.962394             0.976640   \n",
       "4                      0.923456             0.936988             1.147374   \n",
       "\n",
       "parameters  log_gdp_std_hat.167  log_gdp_std_hat.168  log_gdp_std_hat.169  \\\n",
       "draws                                                                       \n",
       "0                      1.065424             0.926426             1.053791   \n",
       "1                      0.813120             0.728859             1.209415   \n",
       "2                      0.931247             0.905352             1.012228   \n",
       "3                      0.939865             0.784410             0.861957   \n",
       "4                      0.666895             0.867448             1.243216   \n",
       "\n",
       "parameters  log_gdp_std_hat.170  \n",
       "draws                            \n",
       "0                      1.037406  \n",
       "1                      1.016214  \n",
       "2                      1.088270  \n",
       "3                      1.069466  \n",
       "4                      1.132200  \n",
       "\n",
       "[5 rows x 520 columns]"
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   "source": [
    "# Transform to dataframe pandas\n",
    "df_samples = samples.to_frame()\n",
    "df_samples.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee7f8791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stan_fit to arviz_stan\n",
    "\n",
    "stan_data = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_predictive=\"log_gdp_std_hat\",\n",
    "    observed_data=['log_gdp_std'],\n",
    "    prior=samples,\n",
    "    prior_model=posteriori,\n",
    "    posterior_model=posteriori,\n",
    ")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ad3f01e",
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   "outputs": [
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (chain: 4, draw: 1000, mu_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 0 1 2 3\n",
       "  * draw      (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * mu_dim_0  (mu_dim_0) int64 0 1 2 3 4 5 6 7 ... 163 164 165 166 167 168 169\n",
       "Data variables:\n",
       "    alpha     (chain, draw) float64 1.005 0.9998 0.9991 ... 0.9894 0.9894 1.002\n",
       "    beta      (chain, draw) float64 0.08217 -0.08118 ... -0.09797 0.09237\n",
       "    sigma     (chain, draw) float64 0.1379 0.1386 0.1325 ... 0.1491 0.1545 0.129\n",
       "    mu        (chain, draw, mu_dim_0) float64 0.9984 1.032 0.9973 ... 0.9904 1.0\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.275185\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/ujreyjxj\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fa06fe57-569b-43d2-b293-0696624f6592' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fa06fe57-569b-43d2-b293-0696624f6592' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5bf0a5f3-c15a-4c1d-adee-8d8ebbd43444' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5bf0a5f3-c15a-4c1d-adee-8d8ebbd43444' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e471fd6a-bf4e-4947-a48b-daea87b41129' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e471fd6a-bf4e-4947-a48b-daea87b41129' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e08e5f59-2a00-43b0-ab79-45d5e8cafeb4' class='xr-var-data-in' type='checkbox'><label for='data-e08e5f59-2a00-43b0-ab79-45d5e8cafeb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-7e1c078c-a17e-4a41-a935-b5453b31a510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e1c078c-a17e-4a41-a935-b5453b31a510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6a373aa-3dde-4480-b536-5a6d6d9cc9d7' class='xr-var-data-in' type='checkbox'><label for='data-f6a373aa-3dde-4480-b536-5a6d6d9cc9d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-9770fff8-a450-443e-b7ff-1cd014e4bb00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9770fff8-a450-443e-b7ff-1cd014e4bb00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-858b89ab-d727-46f0-b43f-5e2f6f88ad1c' class='xr-var-data-in' type='checkbox'><label for='data-858b89ab-d727-46f0-b43f-5e2f6f88ad1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ccfaab7e-389f-4be1-8a63-7ab5183e74fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccfaab7e-389f-4be1-8a63-7ab5183e74fc' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.005 0.9998 ... 0.9894 1.002</div><input id='attrs-6c0d0286-159a-47d1-bc7d-79d175b7f4c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c0d0286-159a-47d1-bc7d-79d175b7f4c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eddcff56-11a1-4955-aa80-e3437cc8c096' class='xr-var-data-in' type='checkbox'><label for='data-eddcff56-11a1-4955-aa80-e3437cc8c096' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.00473223, 0.99982888, 0.99908417, ..., 1.0045643 , 1.00278343,\n",
       "        0.99161365],\n",
       "       [0.99792895, 0.98832873, 1.00010183, ..., 0.98842512, 0.99288424,\n",
       "        1.00707243],\n",
       "       [1.01196708, 0.99552314, 0.99957745, ..., 0.98969026, 1.00551518,\n",
       "        0.99753063],\n",
       "       [0.99063578, 1.00800144, 1.0083135 , ..., 0.98936538, 0.98935462,\n",
       "        1.00233833]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08217 -0.08118 ... 0.09237</div><input id='attrs-23a0400c-1ed2-4ab1-9ea4-38aa59d39054' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23a0400c-1ed2-4ab1-9ea4-38aa59d39054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb5af9ac-06a2-4021-9897-7f1d90190127' class='xr-var-data-in' type='checkbox'><label for='data-eb5af9ac-06a2-4021-9897-7f1d90190127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.08216991, -0.08117614,  0.08771674, ...,  0.07421196,\n",
       "         0.0327339 ,  0.01719223],\n",
       "       [-0.05458121, -0.00319645,  0.02926451, ..., -0.0536412 ,\n",
       "         0.02621705, -0.01392129],\n",
       "       [ 0.01317534, -0.01053841, -0.03933365, ...,  0.03837652,\n",
       "         0.0659751 , -0.0990699 ],\n",
       "       [ 0.0117011 ,  0.05126106,  0.11974149, ..., -0.00276514,\n",
       "        -0.09796807,  0.09236634]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1379 0.1386 ... 0.1545 0.129</div><input id='attrs-85abd052-baab-4778-a770-525b85e783ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85abd052-baab-4778-a770-525b85e783ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127f1854-2280-4e91-9397-d96b7a5e5ed1' class='xr-var-data-in' type='checkbox'><label for='data-127f1854-2280-4e91-9397-d96b7a5e5ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13787819, 0.13857849, 0.13248422, ..., 0.13135254, 0.13367696,\n",
       "        0.12791147],\n",
       "       [0.12885242, 0.13415594, 0.13449398, ..., 0.13519555, 0.13820903,\n",
       "        0.13613249],\n",
       "       [0.14317087, 0.14079542, 0.14165402, ..., 0.13528949, 0.13116656,\n",
       "        0.1400134 ],\n",
       "       [0.14895635, 0.14653902, 0.14429679, ..., 0.1491239 , 0.15447437,\n",
       "        0.12902243]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9984 1.032 0.9973 ... 0.9904 1.0</div><input id='attrs-6448aa2c-63e8-4f66-ad18-b51846ea2783' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6448aa2c-63e8-4f66-ad18-b51846ea2783' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6395e618-86bc-45a5-a62e-ad4d6c7c7875' class='xr-var-data-in' type='checkbox'><label for='data-6395e618-86bc-45a5-a62e-ad4d6c7c7875' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.99843657, 1.03247309, 0.99725741, ..., 1.01040036,\n",
       "         0.99413066, 1.00288821],\n",
       "        [1.0060484 , 0.97242352, 1.0072133 , ..., 0.9942293 ,\n",
       "         1.01030223, 1.0016506 ],\n",
       "        [0.99236352, 1.02869766, 0.99110477, ..., 1.00513492,\n",
       "         0.98776695, 0.99711567],\n",
       "        ...,\n",
       "        [0.99887836, 1.02961853, 0.9978134 , ..., 1.00968349,\n",
       "         0.99498947, 1.00289887],\n",
       "        [1.00027544, 1.01383451, 0.9998057 , ..., 1.00504143,\n",
       "         0.9985601 , 1.00204883],\n",
       "        [0.99029642, 0.99741781, 0.99004971, ..., 0.99279958,\n",
       "         0.98939551, 0.99122783]],\n",
       "\n",
       "       [[1.00211084, 0.97950214, 1.00289409, ..., 0.99416391,\n",
       "         1.00497103, 0.99915384],\n",
       "        [0.98857363, 0.9872496 , 0.9886195 , ..., 0.98810824,\n",
       "         0.98874114, 0.98840046],\n",
       "        [0.99785966, 1.00998164, 0.99743971, ..., 1.00212052,\n",
       "         0.99632613, 0.99944509],\n",
       "...\n",
       "        [0.98674994, 1.00264631, 0.98619923, ..., 0.99233749,\n",
       "         0.98473892, 0.98882903],\n",
       "        [1.00046033, 1.02778861, 0.99951357, ..., 1.01006618,\n",
       "         0.99700307, 1.0040346 ],\n",
       "        [1.00512113, 0.96408427, 1.0065428 , ..., 0.99069673,\n",
       "         1.01031263, 0.99975391]],\n",
       "\n",
       "       [[0.98973927, 0.99458612, 0.98957136, ..., 0.99144293,\n",
       "         0.98912611, 0.99037319],\n",
       "        [1.00407394, 1.02530736, 1.00333834, ..., 1.01153746,\n",
       "         1.00138774, 1.00685107],\n",
       "        [0.99913919, 1.04873866, 0.99742088, ..., 1.01657334,\n",
       "         0.99286444, 1.00562632],\n",
       "        ...,\n",
       "        [0.98957724, 0.98843186, 0.98961692, ..., 0.98917464,\n",
       "         0.98972214, 0.98942744],\n",
       "        [0.9968607 , 0.95628024, 0.99826656, ..., 0.98259672,\n",
       "         1.00199446, 0.99155317],\n",
       "        [0.99526144, 1.03352154, 0.99393597, ..., 1.00870981,\n",
       "         0.99042122, 1.00026549]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff994a6e-a602-483f-9465-c9be5c704813' class='xr-section-summary-in' type='checkbox'  ><label for='section-ff994a6e-a602-483f-9465-c9be5c704813' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-271c1550-9c93-4b47-a13a-49da7435f733' class='xr-index-data-in' type='checkbox'/><label for='index-271c1550-9c93-4b47-a13a-49da7435f733' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7baa2d6-d7ae-4296-9438-1db4552f016a' class='xr-index-data-in' type='checkbox'/><label for='index-f7baa2d6-d7ae-4296-9438-1db4552f016a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-957d1e8f-c603-4463-8352-7fd84b759633' class='xr-index-data-in' type='checkbox'/><label for='index-957d1e8f-c603-4463-8352-7fd84b759633' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1f636a9-71ce-4a42-bf44-cf47b974e61c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f1f636a9-71ce-4a42-bf44-cf47b974e61c' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.275185</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/ujreyjxj</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta * (rugged_std - rugged_std_average);\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 1);\n",
       "        beta ~ normal(0, 1);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;  // By default, if a variable log_lik is present in the Stan model, it will be retrieved as pointwise log likelihood values.\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000, log_gdp_std_hat_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "  * log_gdp_std_hat_dim_0  (log_gdp_std_hat_dim_0) int64 0 1 2 3 ... 167 168 169\n",
       "Data variables:\n",
       "    log_gdp_std_hat        (chain, draw, log_gdp_std_hat_dim_0) float64 1.064...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.443669\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-de8dbb6b-3125-4bd0-98cc-334b4dcdeecd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de8dbb6b-3125-4bd0-98cc-334b4dcdeecd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_gdp_std_hat_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d590399-d540-4a9a-851b-f452f18953f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d590399-d540-4a9a-851b-f452f18953f6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e5c8f4ce-3e25-42c5-abcd-1295bf65962a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5c8f4ce-3e25-42c5-abcd-1295bf65962a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b860f6f4-f730-4a23-a22e-b1b96c2e6c08' class='xr-var-data-in' type='checkbox'><label for='data-b860f6f4-f730-4a23-a22e-b1b96c2e6c08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-06d804d0-3110-4696-a30c-2cc881f8e023' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06d804d0-3110-4696-a30c-2cc881f8e023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9505b03-0caf-4b81-baf9-0917b596bfa9' class='xr-var-data-in' type='checkbox'><label for='data-a9505b03-0caf-4b81-baf9-0917b596bfa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_hat_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-909f3485-9ba3-4f4d-afaa-0b50515005cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909f3485-9ba3-4f4d-afaa-0b50515005cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca93ecac-f2a2-4aa9-acc5-68a095d880af' class='xr-var-data-in' type='checkbox'><label for='data-ca93ecac-f2a2-4aa9-acc5-68a095d880af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec8a2675-84fc-44ce-9da9-e7b34b581498' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec8a2675-84fc-44ce-9da9-e7b34b581498' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std_hat</span></div><div class='xr-var-dims'>(chain, draw, log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.064 1.291 1.021 ... 1.088 0.8895</div><input id='attrs-e57f5382-1467-41de-9398-01f7d3f5ec14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e57f5382-1467-41de-9398-01f7d3f5ec14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-026bde6d-f357-450d-a939-ff4889ca367d' class='xr-var-data-in' type='checkbox'><label for='data-026bde6d-f357-450d-a939-ff4889ca367d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.06429718, 1.2912623 , 1.02094291, ..., 0.92642588,\n",
       "         1.05379059, 1.0374062 ],\n",
       "        [1.23318208, 1.11686098, 0.88384615, ..., 0.86744842,\n",
       "         1.24321581, 1.13220021],\n",
       "        [0.96351377, 0.89467485, 0.84722323, ..., 1.12409146,\n",
       "         0.71309528, 0.99947355],\n",
       "        ...,\n",
       "        [0.6465047 , 1.18476666, 1.32222526, ..., 1.0200848 ,\n",
       "         1.11514535, 1.20106442],\n",
       "        [0.96243743, 1.21598157, 0.98910468, ..., 0.78257654,\n",
       "         0.79251285, 1.00123202],\n",
       "        [1.27280883, 0.78424707, 0.94471192, ..., 1.06572247,\n",
       "         1.08888115, 1.02344652]],\n",
       "\n",
       "       [[0.81540612, 0.9824177 , 0.85911191, ..., 0.72885946,\n",
       "         1.20941456, 1.01621446],\n",
       "        [0.93863014, 1.11811856, 1.21380365, ..., 1.00969855,\n",
       "         0.89285816, 1.02804305],\n",
       "        [0.93294466, 0.84317292, 0.9031973 , ..., 1.07169692,\n",
       "         1.10442381, 1.09646224],\n",
       "...\n",
       "        [0.97992808, 0.99526157, 1.16211818, ..., 0.95648554,\n",
       "         1.04353585, 0.99245143],\n",
       "        [0.96095504, 0.94451949, 1.19567411, ..., 0.94240044,\n",
       "         1.11766479, 0.89635466],\n",
       "        [0.98060003, 0.88681446, 1.04999393, ..., 1.01229614,\n",
       "         0.88818079, 0.96103848]],\n",
       "\n",
       "       [[0.92945972, 1.08518021, 1.029042  , ..., 0.78441021,\n",
       "         0.86195742, 1.06946624],\n",
       "        [0.93334255, 1.20679484, 0.79685231, ..., 1.10790771,\n",
       "         0.82675186, 0.86987888],\n",
       "        [0.96115096, 1.04082015, 0.97931359, ..., 1.12794219,\n",
       "         0.47930996, 1.15873074],\n",
       "        ...,\n",
       "        [0.90366515, 0.87147894, 1.15478608, ..., 0.92837697,\n",
       "         1.08898721, 0.91566638],\n",
       "        [0.97561348, 1.02754894, 0.87144015, ..., 0.79407274,\n",
       "         1.40117705, 0.95587662],\n",
       "        [1.2441509 , 1.03669902, 1.18089389, ..., 0.8136322 ,\n",
       "         1.08813546, 0.88947109]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76e6ed12-ccd3-4a15-90e9-6e9707c9998b' class='xr-section-summary-in' type='checkbox'  ><label for='section-76e6ed12-ccd3-4a15-90e9-6e9707c9998b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7422b28c-837a-49bc-9460-c15b004d04cf' class='xr-index-data-in' type='checkbox'/><label for='index-7422b28c-837a-49bc-9460-c15b004d04cf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-66daa822-2eb3-4ca4-960f-368bc0fd6cff' class='xr-index-data-in' type='checkbox'/><label for='index-66daa822-2eb3-4ca4-960f-368bc0fd6cff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85301902-c26e-4442-a210-9cfd2dafab3f' class='xr-index-data-in' type='checkbox'/><label for='index-85301902-c26e-4442-a210-9cfd2dafab3f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_hat_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4689aa4-cf98-4df6-b16f-878d965f9058' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4689aa4-cf98-4df6-b16f-878d965f9058' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.443669</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (chain: 4, draw: 1000, log_lik_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain          (chain) int64 0 1 2 3\n",
       "  * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_lik_dim_0  (log_lik_dim_0) int64 0 1 2 3 4 5 ... 164 165 166 167 168 169\n",
       "Data variables:\n",
       "    log_lik        (chain, draw, log_lik_dim_0) float64 0.6917 0.9418 ... 0.9285\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.382809\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3cf4e20a-30bd-4510-a94e-7c49245651b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3cf4e20a-30bd-4510-a94e-7c49245651b8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bac00837-03fc-4cea-abf5-edf6f2b944f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bac00837-03fc-4cea-abf5-edf6f2b944f5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-55f06328-3e55-4832-b703-63c4e880fb08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55f06328-3e55-4832-b703-63c4e880fb08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d093e8a4-0bcc-4c38-9833-f415a464e77b' class='xr-var-data-in' type='checkbox'><label for='data-d093e8a4-0bcc-4c38-9833-f415a464e77b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-4cde79dc-af6c-4584-8a90-63be767b019e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cde79dc-af6c-4584-8a90-63be767b019e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e71b29f-e419-4d3c-9fe6-d4c5068c86b9' class='xr-var-data-in' type='checkbox'><label for='data-8e71b29f-e419-4d3c-9fe6-d4c5068c86b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-1cd26595-5ed7-478c-84f0-29c064b59b63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cd26595-5ed7-478c-84f0-29c064b59b63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49258ee9-4d46-4717-b0a5-c14f8fc1e15d' class='xr-var-data-in' type='checkbox'><label for='data-49258ee9-4d46-4717-b0a5-c14f8fc1e15d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de266bf8-2879-4fe6-a33d-600a10792ddb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de266bf8-2879-4fe6-a33d-600a10792ddb' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6917 0.9418 ... -0.1889 0.9285</div><input id='attrs-5d392ff6-b6c5-4703-bbdd-32fc68a3547d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d392ff6-b6c5-4703-bbdd-32fc68a3547d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-728e1191-4273-4b79-8be5-75dadac7256b' class='xr-var-data-in' type='checkbox'><label for='data-728e1191-4273-4b79-8be5-75dadac7256b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.69171332,  0.94183369,  0.31113553, ...,  0.95483294,\n",
       "         -0.13266033,  0.8755398 ],\n",
       "        [ 0.64181743,  1.05584868,  0.39868387, ...,  0.89018008,\n",
       "         -0.31199142,  0.87775064],\n",
       "        [ 0.74084611,  0.98587971,  0.22829571, ...,  0.96582022,\n",
       "         -0.11591801,  0.92669274],\n",
       "        ...,\n",
       "        [ 0.69940202,  0.98900535,  0.28855353, ...,  0.98968794,\n",
       "         -0.21650283,  0.90494099],\n",
       "        [ 0.68667663,  1.02599018,  0.31803362, ...,  0.95892078,\n",
       "         -0.23140385,  0.89849143],\n",
       "        [ 0.76376383,  1.10487471,  0.18848037, ...,  0.93416379,\n",
       "         -0.19011988,  0.97623325]],\n",
       "\n",
       "       [[ 0.67897887,  1.12359941,  0.32632025, ...,  0.93643961,\n",
       "         -0.38096705,  0.9346818 ],\n",
       "        [ 0.76058272,  1.07575606,  0.21304431, ...,  0.8831134 ,\n",
       "         -0.10950474,  0.95441927],\n",
       "        [ 0.70144995,  1.03075693,  0.29940361, ...,  0.94302611,\n",
       "         -0.19471516,  0.90658494],\n",
       "...\n",
       "        [ 0.76841855,  1.04217805,  0.19560997, ...,  0.89759114,\n",
       "         -0.05291071,  0.94662499],\n",
       "        [ 0.68862097,  0.99687796,  0.30419965, ...,  0.99219531,\n",
       "         -0.24401967,  0.90017032],\n",
       "        [ 0.6459441 ,  1.04706713,  0.39636298, ...,  0.86852956,\n",
       "         -0.29449045,  0.87905685]],\n",
       "\n",
       "       [[ 0.71235756,  0.96510947,  0.28157155, ...,  0.83021133,\n",
       "          0.00872651,  0.86904332],\n",
       "        [ 0.64141155,  0.91615019,  0.3833989 , ...,  0.90961341,\n",
       "         -0.12975391,  0.82013649],\n",
       "        [ 0.67444176,  0.84756948,  0.33231309, ...,  0.93674079,\n",
       "         -0.06128069,  0.83503063],\n",
       "        ...,\n",
       "        [ 0.71264716,  0.9714345 ,  0.28238951, ...,  0.82086132,\n",
       "          0.00420799,  0.87121273],\n",
       "        [ 0.66122554,  0.94728377,  0.35736846, ...,  0.77232826,\n",
       "         -0.07486019,  0.83723736],\n",
       "        [ 0.72780102,  0.98679472,  0.23679077, ...,  0.99935599,\n",
       "         -0.18887761,  0.92846092]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1182cc46-b826-4703-b409-fb172b61376f' class='xr-section-summary-in' type='checkbox'  ><label for='section-1182cc46-b826-4703-b409-fb172b61376f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec2ee299-0228-4082-841f-e1dbd8671ada' class='xr-index-data-in' type='checkbox'/><label for='index-ec2ee299-0228-4082-841f-e1dbd8671ada' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c228590-9ad8-4c36-9a01-0ac012af60bd' class='xr-index-data-in' type='checkbox'/><label for='index-3c228590-9ad8-4c36-9a01-0ac012af60bd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-70945ebc-b59f-4913-8d5c-104d5def8585' class='xr-index-data-in' type='checkbox'/><label for='index-70945ebc-b59f-4913-8d5c-104d5def8585' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5632408e-1131-4709-9f2c-f89ae501dd1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5632408e-1131-4709-9f2c-f89ae501dd1b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.382809</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsc1986867-b791-40a1-953a-2cb301dba294\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsc1986867-b791-40a1-953a-2cb301dba294\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    acceptance_rate  (chain, draw) float64 1.0 0.9556 0.9863 ... 0.8224 0.8556\n",
       "    step_size        (chain, draw) float64 0.7835 0.7835 ... 0.7732 0.7732\n",
       "    tree_depth       (chain, draw) int64 2 3 2 2 2 2 3 2 2 ... 1 2 2 2 2 2 2 2 3\n",
       "    n_steps          (chain, draw) int64 3 7 7 3 3 3 7 3 3 ... 3 3 3 3 3 3 3 3 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -248.6 -249.0 ... -246.1 -243.7\n",
       "    lp               (chain, draw) float64 250.2 250.2 249.9 ... 247.4 249.2\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.326585\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/ujreyjxj\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bafd7f6f-018b-441e-a151-34f41acd546b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bafd7f6f-018b-441e-a151-34f41acd546b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-42c65b97-07eb-4d2f-a7ea-7a923a0d18aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42c65b97-07eb-4d2f-a7ea-7a923a0d18aa' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-886addc8-64c0-4cf2-ab37-eaec145002c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-886addc8-64c0-4cf2-ab37-eaec145002c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac24a99e-a357-46ce-b607-d847fb6c4f8f' class='xr-var-data-in' type='checkbox'><label for='data-ac24a99e-a357-46ce-b607-d847fb6c4f8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ebcff441-9d28-40c8-b322-0fc5600d77b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebcff441-9d28-40c8-b322-0fc5600d77b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3a1734f-a85d-44fb-8f74-ac27368b3d9e' class='xr-var-data-in' type='checkbox'><label for='data-a3a1734f-a85d-44fb-8f74-ac27368b3d9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-167de3f5-b597-48ea-bac2-503972870fe8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-167de3f5-b597-48ea-bac2-503972870fe8' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9556 0.9863 ... 0.8224 0.8556</div><input id='attrs-d5102521-8b34-4a83-9b16-6278b57894aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5102521-8b34-4a83-9b16-6278b57894aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d48767-8dfd-4269-b6ff-abf691fda7b7' class='xr-var-data-in' type='checkbox'><label for='data-36d48767-8dfd-4269-b6ff-abf691fda7b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.        , 0.95555246, 0.98633245, ..., 0.7307337 , 0.96889592,\n",
       "        0.91767618],\n",
       "       [1.        , 0.9573073 , 0.9603459 , ..., 0.9971135 , 0.94326228,\n",
       "        0.89113786],\n",
       "       [1.        , 0.89351158, 0.91928402, ..., 0.99023222, 0.96104743,\n",
       "        0.9022795 ],\n",
       "       [0.86877918, 0.90042167, 0.89532326, ..., 0.89009325, 0.82236223,\n",
       "        0.85556676]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7835 0.7835 ... 0.7732 0.7732</div><input id='attrs-be5a4639-503b-4e81-b115-c7111749aebf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be5a4639-503b-4e81-b115-c7111749aebf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd498e39-0e0d-43dd-b9bf-745cd9a1995e' class='xr-var-data-in' type='checkbox'><label for='data-fd498e39-0e0d-43dd-b9bf-745cd9a1995e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.78350709, 0.78350709, 0.78350709, ..., 0.78350709, 0.78350709,\n",
       "        0.78350709],\n",
       "       [0.82075329, 0.82075329, 0.82075329, ..., 0.82075329, 0.82075329,\n",
       "        0.82075329],\n",
       "       [0.70159087, 0.70159087, 0.70159087, ..., 0.70159087, 0.70159087,\n",
       "        0.70159087],\n",
       "       [0.77315869, 0.77315869, 0.77315869, ..., 0.77315869, 0.77315869,\n",
       "        0.77315869]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 2 2 2 2 3 2 ... 2 2 2 2 2 2 2 3</div><input id='attrs-dd20db93-f639-46fa-8426-7a57b32c9cfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd20db93-f639-46fa-8426-7a57b32c9cfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed756ec3-ee62-4d6a-ac94-4671b8ab526a' class='xr-var-data-in' type='checkbox'><label for='data-ed756ec3-ee62-4d6a-ac94-4671b8ab526a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 3, 2, ..., 3, 2, 3],\n",
       "       [3, 2, 2, ..., 3, 2, 1],\n",
       "       [2, 2, 2, ..., 3, 3, 3],\n",
       "       [2, 2, 2, ..., 2, 2, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 7 7 3 3 3 7 3 ... 3 3 3 3 3 3 3 7</div><input id='attrs-8175ed3f-5cf5-497a-9894-77442b26a779' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8175ed3f-5cf5-497a-9894-77442b26a779' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab7c752e-3c9d-4000-b789-cff432c4c570' class='xr-var-data-in' type='checkbox'><label for='data-ab7c752e-3c9d-4000-b789-cff432c4c570' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 7, 7, ..., 7, 3, 7],\n",
       "       [7, 3, 3, ..., 7, 3, 3],\n",
       "       [3, 3, 3, ..., 7, 7, 7],\n",
       "       [3, 7, 3, ..., 3, 3, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-b5e4741d-b5ac-4ba4-a9d1-2dcc2d5d17d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5e4741d-b5ac-4ba4-a9d1-2dcc2d5d17d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1bd839-541f-4981-963a-3460c8823ea4' class='xr-var-data-in' type='checkbox'><label for='data-5e1bd839-541f-4981-963a-3460c8823ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-248.6 -249.0 ... -246.1 -243.7</div><input id='attrs-ed3dbff1-ca83-4491-ade6-40b1cd822365' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed3dbff1-ca83-4491-ade6-40b1cd822365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9819af73-6e26-424f-923b-9a82b5a96466' class='xr-var-data-in' type='checkbox'><label for='data-9819af73-6e26-424f-923b-9a82b5a96466' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-248.56131464, -248.99594074, -249.79153399, ..., -248.04730834,\n",
       "        -249.69947074, -249.8345168 ],\n",
       "       [-249.60429775, -249.27980196, -250.1293255 , ..., -248.96310469,\n",
       "        -249.34051904, -249.85051263],\n",
       "       [-248.17005814, -248.88285884, -250.10231956, ..., -249.61429531,\n",
       "        -249.50428276, -247.65061608],\n",
       "       [-249.77693429, -248.14042744, -248.08953738, ..., -249.46497472,\n",
       "        -246.12223598, -243.74095217]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>250.2 250.2 249.9 ... 247.4 249.2</div><input id='attrs-229ae238-4247-47a8-bcfd-38b92f9540ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-229ae238-4247-47a8-bcfd-38b92f9540ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01fd1b1b-9ae2-492a-aa21-82803136d9e7' class='xr-var-data-in' type='checkbox'><label for='data-01fd1b1b-9ae2-492a-aa21-82803136d9e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[250.22384816, 250.23804859, 249.90051621, ..., 250.04000329,\n",
       "        251.05530826, 250.12339679],\n",
       "       [250.0980318 , 250.62991427, 251.17123106, ..., 250.18782321,\n",
       "        251.01590948, 251.07210016],\n",
       "       [250.40707327, 251.10781599, 250.89820493, ..., 250.61388701,\n",
       "        250.16469343, 249.66814521],\n",
       "       [249.85996204, 250.00693027, 248.61191486, ..., 249.74336242,\n",
       "        247.4019236 , 249.22789837]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62d35636-5c1d-4bc5-9f35-c700315ac7c5' class='xr-section-summary-in' type='checkbox'  ><label for='section-62d35636-5c1d-4bc5-9f35-c700315ac7c5' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8bf88a6c-f8ca-4d0c-8850-2b5ed39a97f0' class='xr-index-data-in' type='checkbox'/><label for='index-8bf88a6c-f8ca-4d0c-8850-2b5ed39a97f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cb3eb53e-21c6-45c9-860b-b8c5caf66d4e' class='xr-index-data-in' type='checkbox'/><label for='index-cb3eb53e-21c6-45c9-860b-b8c5caf66d4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-925c8587-eb69-4c68-a02c-aa814f57e688' class='xr-section-summary-in' type='checkbox'  ><label for='section-925c8587-eb69-4c68-a02c-aa814f57e688' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.326585</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/ujreyjxj</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta * (rugged_std - rugged_std_average);\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 1);\n",
       "        beta ~ normal(0, 1);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;  // By default, if a variable log_lik is present in the Stan model, it will be retrieved as pointwise log likelihood values.\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_priora52073ed-15ae-4e89-bca2-29437d9ec875\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_priora52073ed-15ae-4e89-bca2-29437d9ec875\" class = \"xr-section-summary\">prior</label>\n",
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       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
       ".xr-header > div,\n",
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       "\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000, mu_dim_0: 170,\n",
       "                            log_lik_dim_0: 170, log_gdp_std_hat_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "  * mu_dim_0               (mu_dim_0) int64 0 1 2 3 4 5 ... 165 166 167 168 169\n",
       "  * log_lik_dim_0          (log_lik_dim_0) int64 0 1 2 3 4 ... 166 167 168 169\n",
       "  * log_gdp_std_hat_dim_0  (log_gdp_std_hat_dim_0) int64 0 1 2 3 ... 167 168 169\n",
       "Data variables:\n",
       "    alpha                  (chain, draw) float64 1.005 0.9998 ... 0.9894 1.002\n",
       "    beta                   (chain, draw) float64 0.08217 -0.08118 ... 0.09237\n",
       "    sigma                  (chain, draw) float64 0.1379 0.1386 ... 0.1545 0.129\n",
       "    mu                     (chain, draw, mu_dim_0) float64 0.9984 1.032 ... 1.0\n",
       "    log_lik                (chain, draw, log_lik_dim_0) float64 0.6917 ... 0....\n",
       "    log_gdp_std_hat        (chain, draw, log_gdp_std_hat_dim_0) float64 1.064...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.500663\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/ujreyjxj\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7a935abb-5b15-4b34-824a-120b1651a677' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a935abb-5b15-4b34-824a-120b1651a677' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 170</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 170</li><li><span class='xr-has-index'>log_gdp_std_hat_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5c30d9f8-03df-405a-a661-497870964c77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c30d9f8-03df-405a-a661-497870964c77' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-7f44b880-178a-4d67-a48d-9178052daabe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f44b880-178a-4d67-a48d-9178052daabe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfb281d5-8b09-4d68-b327-0a695f2b5fc0' class='xr-var-data-in' type='checkbox'><label for='data-dfb281d5-8b09-4d68-b327-0a695f2b5fc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-de112195-cefc-4b53-b998-3c0529be30cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de112195-cefc-4b53-b998-3c0529be30cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdd48d4e-cd28-4819-8fed-5f63361b7dfc' class='xr-var-data-in' type='checkbox'><label for='data-cdd48d4e-cd28-4819-8fed-5f63361b7dfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-84a2cf2e-1f63-401e-b858-f1ea536365b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84a2cf2e-1f63-401e-b858-f1ea536365b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98e88c1-2f9c-4c8e-8d57-a37364361b3a' class='xr-var-data-in' type='checkbox'><label for='data-b98e88c1-2f9c-4c8e-8d57-a37364361b3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-f2305f4f-c9b6-489d-af45-167d57762e67' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2305f4f-c9b6-489d-af45-167d57762e67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e4e7a6-5994-470e-9dee-3c5b07603f8a' class='xr-var-data-in' type='checkbox'><label for='data-51e4e7a6-5994-470e-9dee-3c5b07603f8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_hat_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-190aeb26-ae56-4143-a90e-68b125548412' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-190aeb26-ae56-4143-a90e-68b125548412' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3afcf3b-92e8-4bf5-9080-0963be154fb0' class='xr-var-data-in' type='checkbox'><label for='data-a3afcf3b-92e8-4bf5-9080-0963be154fb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4ab9a33-5cd6-428a-84d9-7a311b19c582' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4ab9a33-5cd6-428a-84d9-7a311b19c582' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.005 0.9998 ... 0.9894 1.002</div><input id='attrs-069354fa-1983-4774-9bb8-513c184bf7fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-069354fa-1983-4774-9bb8-513c184bf7fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78cf4eb6-e471-487d-8ebf-8a3c6e727418' class='xr-var-data-in' type='checkbox'><label for='data-78cf4eb6-e471-487d-8ebf-8a3c6e727418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.00473223, 0.99982888, 0.99908417, ..., 1.0045643 , 1.00278343,\n",
       "        0.99161365],\n",
       "       [0.99792895, 0.98832873, 1.00010183, ..., 0.98842512, 0.99288424,\n",
       "        1.00707243],\n",
       "       [1.01196708, 0.99552314, 0.99957745, ..., 0.98969026, 1.00551518,\n",
       "        0.99753063],\n",
       "       [0.99063578, 1.00800144, 1.0083135 , ..., 0.98936538, 0.98935462,\n",
       "        1.00233833]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08217 -0.08118 ... 0.09237</div><input id='attrs-ef257a81-0b7d-4bba-a16f-251425a1afeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef257a81-0b7d-4bba-a16f-251425a1afeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02a4aaf0-de23-47ea-9a08-20363d6c3122' class='xr-var-data-in' type='checkbox'><label for='data-02a4aaf0-de23-47ea-9a08-20363d6c3122' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.08216991, -0.08117614,  0.08771674, ...,  0.07421196,\n",
       "         0.0327339 ,  0.01719223],\n",
       "       [-0.05458121, -0.00319645,  0.02926451, ..., -0.0536412 ,\n",
       "         0.02621705, -0.01392129],\n",
       "       [ 0.01317534, -0.01053841, -0.03933365, ...,  0.03837652,\n",
       "         0.0659751 , -0.0990699 ],\n",
       "       [ 0.0117011 ,  0.05126106,  0.11974149, ..., -0.00276514,\n",
       "        -0.09796807,  0.09236634]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1379 0.1386 ... 0.1545 0.129</div><input id='attrs-85637015-42ea-4ab5-be49-d4499d9dd8db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85637015-42ea-4ab5-be49-d4499d9dd8db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfac8e81-05bb-4fd9-a8d5-2886cb3d36e7' class='xr-var-data-in' type='checkbox'><label for='data-dfac8e81-05bb-4fd9-a8d5-2886cb3d36e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13787819, 0.13857849, 0.13248422, ..., 0.13135254, 0.13367696,\n",
       "        0.12791147],\n",
       "       [0.12885242, 0.13415594, 0.13449398, ..., 0.13519555, 0.13820903,\n",
       "        0.13613249],\n",
       "       [0.14317087, 0.14079542, 0.14165402, ..., 0.13528949, 0.13116656,\n",
       "        0.1400134 ],\n",
       "       [0.14895635, 0.14653902, 0.14429679, ..., 0.1491239 , 0.15447437,\n",
       "        0.12902243]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9984 1.032 0.9973 ... 0.9904 1.0</div><input id='attrs-efd0811c-28a3-4f08-be52-2665b9f76f97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efd0811c-28a3-4f08-be52-2665b9f76f97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fbf8cba-ffe6-431b-ab31-4e7aef08d910' class='xr-var-data-in' type='checkbox'><label for='data-9fbf8cba-ffe6-431b-ab31-4e7aef08d910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.99843657, 1.03247309, 0.99725741, ..., 1.01040036,\n",
       "         0.99413066, 1.00288821],\n",
       "        [1.0060484 , 0.97242352, 1.0072133 , ..., 0.9942293 ,\n",
       "         1.01030223, 1.0016506 ],\n",
       "        [0.99236352, 1.02869766, 0.99110477, ..., 1.00513492,\n",
       "         0.98776695, 0.99711567],\n",
       "        ...,\n",
       "        [0.99887836, 1.02961853, 0.9978134 , ..., 1.00968349,\n",
       "         0.99498947, 1.00289887],\n",
       "        [1.00027544, 1.01383451, 0.9998057 , ..., 1.00504143,\n",
       "         0.9985601 , 1.00204883],\n",
       "        [0.99029642, 0.99741781, 0.99004971, ..., 0.99279958,\n",
       "         0.98939551, 0.99122783]],\n",
       "\n",
       "       [[1.00211084, 0.97950214, 1.00289409, ..., 0.99416391,\n",
       "         1.00497103, 0.99915384],\n",
       "        [0.98857363, 0.9872496 , 0.9886195 , ..., 0.98810824,\n",
       "         0.98874114, 0.98840046],\n",
       "        [0.99785966, 1.00998164, 0.99743971, ..., 1.00212052,\n",
       "         0.99632613, 0.99944509],\n",
       "...\n",
       "        [0.98674994, 1.00264631, 0.98619923, ..., 0.99233749,\n",
       "         0.98473892, 0.98882903],\n",
       "        [1.00046033, 1.02778861, 0.99951357, ..., 1.01006618,\n",
       "         0.99700307, 1.0040346 ],\n",
       "        [1.00512113, 0.96408427, 1.0065428 , ..., 0.99069673,\n",
       "         1.01031263, 0.99975391]],\n",
       "\n",
       "       [[0.98973927, 0.99458612, 0.98957136, ..., 0.99144293,\n",
       "         0.98912611, 0.99037319],\n",
       "        [1.00407394, 1.02530736, 1.00333834, ..., 1.01153746,\n",
       "         1.00138774, 1.00685107],\n",
       "        [0.99913919, 1.04873866, 0.99742088, ..., 1.01657334,\n",
       "         0.99286444, 1.00562632],\n",
       "        ...,\n",
       "        [0.98957724, 0.98843186, 0.98961692, ..., 0.98917464,\n",
       "         0.98972214, 0.98942744],\n",
       "        [0.9968607 , 0.95628024, 0.99826656, ..., 0.98259672,\n",
       "         1.00199446, 0.99155317],\n",
       "        [0.99526144, 1.03352154, 0.99393597, ..., 1.00870981,\n",
       "         0.99042122, 1.00026549]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6917 0.9418 ... -0.1889 0.9285</div><input id='attrs-cf2f1ee4-412f-4d40-a5ce-d991673838d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf2f1ee4-412f-4d40-a5ce-d991673838d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d61d772c-e88c-4436-b100-03b6138467d1' class='xr-var-data-in' type='checkbox'><label for='data-d61d772c-e88c-4436-b100-03b6138467d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.69171332,  0.94183369,  0.31113553, ...,  0.95483294,\n",
       "         -0.13266033,  0.8755398 ],\n",
       "        [ 0.64181743,  1.05584868,  0.39868387, ...,  0.89018008,\n",
       "         -0.31199142,  0.87775064],\n",
       "        [ 0.74084611,  0.98587971,  0.22829571, ...,  0.96582022,\n",
       "         -0.11591801,  0.92669274],\n",
       "        ...,\n",
       "        [ 0.69940202,  0.98900535,  0.28855353, ...,  0.98968794,\n",
       "         -0.21650283,  0.90494099],\n",
       "        [ 0.68667663,  1.02599018,  0.31803362, ...,  0.95892078,\n",
       "         -0.23140385,  0.89849143],\n",
       "        [ 0.76376383,  1.10487471,  0.18848037, ...,  0.93416379,\n",
       "         -0.19011988,  0.97623325]],\n",
       "\n",
       "       [[ 0.67897887,  1.12359941,  0.32632025, ...,  0.93643961,\n",
       "         -0.38096705,  0.9346818 ],\n",
       "        [ 0.76058272,  1.07575606,  0.21304431, ...,  0.8831134 ,\n",
       "         -0.10950474,  0.95441927],\n",
       "        [ 0.70144995,  1.03075693,  0.29940361, ...,  0.94302611,\n",
       "         -0.19471516,  0.90658494],\n",
       "...\n",
       "        [ 0.76841855,  1.04217805,  0.19560997, ...,  0.89759114,\n",
       "         -0.05291071,  0.94662499],\n",
       "        [ 0.68862097,  0.99687796,  0.30419965, ...,  0.99219531,\n",
       "         -0.24401967,  0.90017032],\n",
       "        [ 0.6459441 ,  1.04706713,  0.39636298, ...,  0.86852956,\n",
       "         -0.29449045,  0.87905685]],\n",
       "\n",
       "       [[ 0.71235756,  0.96510947,  0.28157155, ...,  0.83021133,\n",
       "          0.00872651,  0.86904332],\n",
       "        [ 0.64141155,  0.91615019,  0.3833989 , ...,  0.90961341,\n",
       "         -0.12975391,  0.82013649],\n",
       "        [ 0.67444176,  0.84756948,  0.33231309, ...,  0.93674079,\n",
       "         -0.06128069,  0.83503063],\n",
       "        ...,\n",
       "        [ 0.71264716,  0.9714345 ,  0.28238951, ...,  0.82086132,\n",
       "          0.00420799,  0.87121273],\n",
       "        [ 0.66122554,  0.94728377,  0.35736846, ...,  0.77232826,\n",
       "         -0.07486019,  0.83723736],\n",
       "        [ 0.72780102,  0.98679472,  0.23679077, ...,  0.99935599,\n",
       "         -0.18887761,  0.92846092]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std_hat</span></div><div class='xr-var-dims'>(chain, draw, log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.064 1.291 1.021 ... 1.088 0.8895</div><input id='attrs-10aa6c02-1101-483b-b9ef-b190fc88a565' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10aa6c02-1101-483b-b9ef-b190fc88a565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e74a3eee-870e-4089-9955-edf76f806c79' class='xr-var-data-in' type='checkbox'><label for='data-e74a3eee-870e-4089-9955-edf76f806c79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.06429718, 1.2912623 , 1.02094291, ..., 0.92642588,\n",
       "         1.05379059, 1.0374062 ],\n",
       "        [1.23318208, 1.11686098, 0.88384615, ..., 0.86744842,\n",
       "         1.24321581, 1.13220021],\n",
       "        [0.96351377, 0.89467485, 0.84722323, ..., 1.12409146,\n",
       "         0.71309528, 0.99947355],\n",
       "        ...,\n",
       "        [0.6465047 , 1.18476666, 1.32222526, ..., 1.0200848 ,\n",
       "         1.11514535, 1.20106442],\n",
       "        [0.96243743, 1.21598157, 0.98910468, ..., 0.78257654,\n",
       "         0.79251285, 1.00123202],\n",
       "        [1.27280883, 0.78424707, 0.94471192, ..., 1.06572247,\n",
       "         1.08888115, 1.02344652]],\n",
       "\n",
       "       [[0.81540612, 0.9824177 , 0.85911191, ..., 0.72885946,\n",
       "         1.20941456, 1.01621446],\n",
       "        [0.93863014, 1.11811856, 1.21380365, ..., 1.00969855,\n",
       "         0.89285816, 1.02804305],\n",
       "        [0.93294466, 0.84317292, 0.9031973 , ..., 1.07169692,\n",
       "         1.10442381, 1.09646224],\n",
       "...\n",
       "        [0.97992808, 0.99526157, 1.16211818, ..., 0.95648554,\n",
       "         1.04353585, 0.99245143],\n",
       "        [0.96095504, 0.94451949, 1.19567411, ..., 0.94240044,\n",
       "         1.11766479, 0.89635466],\n",
       "        [0.98060003, 0.88681446, 1.04999393, ..., 1.01229614,\n",
       "         0.88818079, 0.96103848]],\n",
       "\n",
       "       [[0.92945972, 1.08518021, 1.029042  , ..., 0.78441021,\n",
       "         0.86195742, 1.06946624],\n",
       "        [0.93334255, 1.20679484, 0.79685231, ..., 1.10790771,\n",
       "         0.82675186, 0.86987888],\n",
       "        [0.96115096, 1.04082015, 0.97931359, ..., 1.12794219,\n",
       "         0.47930996, 1.15873074],\n",
       "        ...,\n",
       "        [0.90366515, 0.87147894, 1.15478608, ..., 0.92837697,\n",
       "         1.08898721, 0.91566638],\n",
       "        [0.97561348, 1.02754894, 0.87144015, ..., 0.79407274,\n",
       "         1.40117705, 0.95587662],\n",
       "        [1.2441509 , 1.03669902, 1.18089389, ..., 0.8136322 ,\n",
       "         1.08813546, 0.88947109]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2a20ced-506f-4c0f-8b7d-72dcb7c96cd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2a20ced-506f-4c0f-8b7d-72dcb7c96cd6' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d0c936b5-0c60-429d-b1c2-012f6f5ac64b' class='xr-index-data-in' type='checkbox'/><label for='index-d0c936b5-0c60-429d-b1c2-012f6f5ac64b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c5399e7-12d0-43d1-bffe-4947f14b2dc8' class='xr-index-data-in' type='checkbox'/><label for='index-6c5399e7-12d0-43d1-bffe-4947f14b2dc8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-388935bb-5920-4458-903f-abdfff215fec' class='xr-index-data-in' type='checkbox'/><label for='index-388935bb-5920-4458-903f-abdfff215fec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0a3138fe-83a8-485a-9f74-42aa580dba55' class='xr-index-data-in' type='checkbox'/><label for='index-0a3138fe-83a8-485a-9f74-42aa580dba55' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56c53716-b2c0-4966-b55a-32736bdcc05f' class='xr-index-data-in' type='checkbox'/><label for='index-56c53716-b2c0-4966-b55a-32736bdcc05f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_hat_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79042141-f443-4a9a-9b23-e56dc43be407' class='xr-section-summary-in' type='checkbox'  ><label for='section-79042141-f443-4a9a-9b23-e56dc43be407' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.500663</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/ujreyjxj</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta * (rugged_std - rugged_std_average);\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 1);\n",
       "        beta ~ normal(0, 1);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;  // By default, if a variable log_lik is present in the Stan model, it will be retrieved as pointwise log likelihood values.\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats_priorb74d469d-c736-413b-9e65-c6f21e60f658\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats_priorb74d469d-c736-413b-9e65-c6f21e60f658\" class = \"xr-section-summary\">sample_stats_prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 250.2 250.2 249.9 ... 247.4 249.2\n",
       "    acceptance_rate  (chain, draw) float64 1.0 0.9556 0.9863 ... 0.8224 0.8556\n",
       "    step_size        (chain, draw) float64 0.7835 0.7835 ... 0.7732 0.7732\n",
       "    tree_depth       (chain, draw) int64 2 3 2 2 2 2 3 2 2 ... 1 2 2 2 2 2 2 2 3\n",
       "    n_steps          (chain, draw) int64 3 7 7 3 3 3 7 3 3 ... 3 3 3 3 3 3 3 3 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -248.6 -249.0 ... -246.1 -243.7\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.652569\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/ujreyjxj\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-29ebc210-3ed5-486b-a2a0-3b116c84212c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29ebc210-3ed5-486b-a2a0-3b116c84212c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9ff5201f-c9cb-4980-999b-c90fe91b5e70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9ff5201f-c9cb-4980-999b-c90fe91b5e70' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-41340407-58ce-4918-b125-97732ad1c16b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41340407-58ce-4918-b125-97732ad1c16b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a091e4e1-e3fe-47c4-95cb-744d2a7c3e45' class='xr-var-data-in' type='checkbox'><label for='data-a091e4e1-e3fe-47c4-95cb-744d2a7c3e45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-f4a10610-6b7b-4c94-a8da-26a88c42fb85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4a10610-6b7b-4c94-a8da-26a88c42fb85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-447464ab-3706-4159-9f74-92e07af5d1d8' class='xr-var-data-in' type='checkbox'><label for='data-447464ab-3706-4159-9f74-92e07af5d1d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fddd6e04-ac8c-4286-b741-13ae456792f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fddd6e04-ac8c-4286-b741-13ae456792f9' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>250.2 250.2 249.9 ... 247.4 249.2</div><input id='attrs-9675281d-6517-4de2-8d88-94ead35945c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9675281d-6517-4de2-8d88-94ead35945c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10fa5d75-ddf4-407a-8e9e-bd28fb0716aa' class='xr-var-data-in' type='checkbox'><label for='data-10fa5d75-ddf4-407a-8e9e-bd28fb0716aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[250.22384816, 250.23804859, 249.90051621, ..., 250.04000329,\n",
       "        251.05530826, 250.12339679],\n",
       "       [250.0980318 , 250.62991427, 251.17123106, ..., 250.18782321,\n",
       "        251.01590948, 251.07210016],\n",
       "       [250.40707327, 251.10781599, 250.89820493, ..., 250.61388701,\n",
       "        250.16469343, 249.66814521],\n",
       "       [249.85996204, 250.00693027, 248.61191486, ..., 249.74336242,\n",
       "        247.4019236 , 249.22789837]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9556 0.9863 ... 0.8224 0.8556</div><input id='attrs-925b8f04-9285-4498-a3c5-71db87170333' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-925b8f04-9285-4498-a3c5-71db87170333' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da7910e-cf2c-46c4-8df2-1f26c2e8240d' class='xr-var-data-in' type='checkbox'><label for='data-0da7910e-cf2c-46c4-8df2-1f26c2e8240d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.        , 0.95555246, 0.98633245, ..., 0.7307337 , 0.96889592,\n",
       "        0.91767618],\n",
       "       [1.        , 0.9573073 , 0.9603459 , ..., 0.9971135 , 0.94326228,\n",
       "        0.89113786],\n",
       "       [1.        , 0.89351158, 0.91928402, ..., 0.99023222, 0.96104743,\n",
       "        0.9022795 ],\n",
       "       [0.86877918, 0.90042167, 0.89532326, ..., 0.89009325, 0.82236223,\n",
       "        0.85556676]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7835 0.7835 ... 0.7732 0.7732</div><input id='attrs-dfbef174-f5be-473d-86de-ff9c16da467d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfbef174-f5be-473d-86de-ff9c16da467d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15937e3d-f419-4c74-b225-71e9fa777617' class='xr-var-data-in' type='checkbox'><label for='data-15937e3d-f419-4c74-b225-71e9fa777617' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.78350709, 0.78350709, 0.78350709, ..., 0.78350709, 0.78350709,\n",
       "        0.78350709],\n",
       "       [0.82075329, 0.82075329, 0.82075329, ..., 0.82075329, 0.82075329,\n",
       "        0.82075329],\n",
       "       [0.70159087, 0.70159087, 0.70159087, ..., 0.70159087, 0.70159087,\n",
       "        0.70159087],\n",
       "       [0.77315869, 0.77315869, 0.77315869, ..., 0.77315869, 0.77315869,\n",
       "        0.77315869]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 2 2 2 2 3 2 ... 2 2 2 2 2 2 2 3</div><input id='attrs-1a19d10f-3cb6-4315-863b-594ba3025072' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a19d10f-3cb6-4315-863b-594ba3025072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52d70abe-b9d9-4f2a-b201-554f179bf094' class='xr-var-data-in' type='checkbox'><label for='data-52d70abe-b9d9-4f2a-b201-554f179bf094' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 3, 2, ..., 3, 2, 3],\n",
       "       [3, 2, 2, ..., 3, 2, 1],\n",
       "       [2, 2, 2, ..., 3, 3, 3],\n",
       "       [2, 2, 2, ..., 2, 2, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 7 7 3 3 3 7 3 ... 3 3 3 3 3 3 3 7</div><input id='attrs-8a17370d-6c42-4dab-a79d-32cb7c95aece' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a17370d-6c42-4dab-a79d-32cb7c95aece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba081368-82b7-4f47-b415-f3af5c5450a6' class='xr-var-data-in' type='checkbox'><label for='data-ba081368-82b7-4f47-b415-f3af5c5450a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 7, 7, ..., 7, 3, 7],\n",
       "       [7, 3, 3, ..., 7, 3, 3],\n",
       "       [3, 3, 3, ..., 7, 7, 7],\n",
       "       [3, 7, 3, ..., 3, 3, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e3d9d7e4-cafc-4413-b959-d016889c5cf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3d9d7e4-cafc-4413-b959-d016889c5cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eac2a57e-b2f0-467e-b2c8-ba1e524452d2' class='xr-var-data-in' type='checkbox'><label for='data-eac2a57e-b2f0-467e-b2c8-ba1e524452d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-248.6 -249.0 ... -246.1 -243.7</div><input id='attrs-13a1618a-d0a9-4444-962c-248d8ff4d3b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13a1618a-d0a9-4444-962c-248d8ff4d3b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c818617-d6db-47c4-b16b-9740efc76597' class='xr-var-data-in' type='checkbox'><label for='data-1c818617-d6db-47c4-b16b-9740efc76597' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-248.56131464, -248.99594074, -249.79153399, ..., -248.04730834,\n",
       "        -249.69947074, -249.8345168 ],\n",
       "       [-249.60429775, -249.27980196, -250.1293255 , ..., -248.96310469,\n",
       "        -249.34051904, -249.85051263],\n",
       "       [-248.17005814, -248.88285884, -250.10231956, ..., -249.61429531,\n",
       "        -249.50428276, -247.65061608],\n",
       "       [-249.77693429, -248.14042744, -248.08953738, ..., -249.46497472,\n",
       "        -246.12223598, -243.74095217]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3956a9e-7e0a-438f-8033-5b1e219be3c6' class='xr-section-summary-in' type='checkbox'  ><label for='section-d3956a9e-7e0a-438f-8033-5b1e219be3c6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-27faf6c9-75a0-423b-afab-7ced4303934b' class='xr-index-data-in' type='checkbox'/><label for='index-27faf6c9-75a0-423b-afab-7ced4303934b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-55b88e48-b79e-4fc8-b2e6-55b06daefaa5' class='xr-index-data-in' type='checkbox'/><label for='index-55b88e48-b79e-4fc8-b2e6-55b06daefaa5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1553899-947c-4f6c-b4fb-4e31121d59e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1553899-947c-4f6c-b4fb-4e31121d59e8' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.652569</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/ujreyjxj</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta * (rugged_std - rugged_std_average);\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 1);\n",
       "        beta ~ normal(0, 1);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;  // By default, if a variable log_lik is present in the Stan model, it will be retrieved as pointwise log likelihood values.\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
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       "                  <input id=\"idata_observed_data76b571f4-d247-4ab1-a74f-2b83e6395e11\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data76b571f4-d247-4ab1-a74f-2b83e6395e11\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
       ".xr-wrap {\n",
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       "  min-width: 300px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (log_gdp_std_dim_0: 170)\n",
       "Coordinates:\n",
       "  * log_gdp_std_dim_0  (log_gdp_std_dim_0) int64 0 1 2 3 4 ... 166 167 168 169\n",
       "Data variables:\n",
       "    log_gdp_std        (log_gdp_std_dim_0) float64 0.8797 0.9648 ... 0.9186\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:20.241424\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3d17b8b5-d597-4dfc-8461-44c4dabfc065' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3d17b8b5-d597-4dfc-8461-44c4dabfc065' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>log_gdp_std_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0f562830-5156-44e8-9742-c9d79eb57539' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f562830-5156-44e8-9742-c9d79eb57539' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-95b2286c-52da-4386-9253-817c6b2afca1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95b2286c-52da-4386-9253-817c6b2afca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b762636-3bae-47ca-aa20-f52ef2082ba0' class='xr-var-data-in' type='checkbox'><label for='data-9b762636-3bae-47ca-aa20-f52ef2082ba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d67e9120-77df-456d-b18f-bd9db5c20639' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d67e9120-77df-456d-b18f-bd9db5c20639' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std</span></div><div class='xr-var-dims'>(log_gdp_std_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8797 0.9648 ... 0.781 0.9186</div><input id='attrs-8faab453-0e50-4e6f-97b4-c88ad731fe57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8faab453-0e50-4e6f-97b4-c88ad731fe57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33b22efb-6edc-450d-97ab-e01af97aff7f' class='xr-var-data-in' type='checkbox'><label for='data-33b22efb-6edc-450d-97ab-e01af97aff7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.87971187, 0.9647547 , 1.1662705 , 1.10448536, 0.91490375,\n",
       "       1.0816501 , 1.19091827, 1.20635083, 0.92191149, 0.75522974,\n",
       "       1.19932025, 0.80615567, 0.81085767, 0.85700117, 1.02100708,\n",
       "       1.13604626, 1.14353554, 1.00673891, 0.99526755, 1.01882817,\n",
       "       0.91377709, 1.04445526, 1.13125035, 1.05074296, 0.82725418,\n",
       "       1.1992612 , 1.21101002, 1.07051814, 0.97168624, 0.86442499,\n",
       "       0.88532949, 0.76378895, 0.80593281, 1.02608907, 0.87809731,\n",
       "       0.99665294, 1.06397383, 1.16462671, 1.13188329, 1.19121349,\n",
       "       0.88528458, 1.02007148, 1.20538601, 1.0291909 , 1.0094337 ,\n",
       "       0.95380637, 0.96138451, 0.80021355, 1.17270317, 1.0785727 ,\n",
       "       0.7578708 , 1.19154684, 0.99882645, 1.19220721, 1.02371474,\n",
       "       1.19506889, 0.8851899 , 0.88595898, 0.89099694, 0.87527671,\n",
       "       0.78224406, 1.13047194, 1.14636797, 1.04818317, 0.97305583,\n",
       "       0.97590885, 1.19265328, 0.91891832, 1.07593003, 0.87995217,\n",
       "       1.11419863, 0.94113391, 0.91456917, 1.2124455 , 1.01797366,\n",
       "       1.20611401, 1.18348401, 1.1889489 , 0.96307105, 0.96922103,\n",
       "       1.19456727, 0.99007688, 0.81322267, 0.86325463, 0.8838469 ,\n",
       "       1.09495636, 1.13783355, 1.1362733 , 0.86399746, 0.97999287,\n",
       "       1.01374697, 0.96226885, 0.89940883, 1.06594471, 1.28736127,\n",
       "       1.05377081, 1.15413994, 0.95859462, 0.84465573, 0.78849201,\n",
       "       1.06962336, 1.0225778 , 0.78181112, 1.15206046, 0.86771734,\n",
       "       0.79561618, 0.8753453 , 1.0768738 , 0.74834587, 1.06805786,\n",
       "       1.0225421 , 1.17470978, 0.76969203, 0.79637985, 0.95042471,\n",
       "       1.20481088, 1.22579142, 0.84389598, 1.16049201, 1.10972919,\n",
       "       0.8879629 , 1.02458815, 0.99330541, 0.9746153 , 0.91014275,\n",
       "       1.08601201, 1.17525067, 1.15205757, 0.98901498, 1.18678542,\n",
       "       1.0192294 , 1.04110842, 0.81555521, 1.10640269, 0.87214242,\n",
       "       0.85345509, 1.18226546, 0.88406187, 0.72155551, 0.99007276,\n",
       "       1.09578033, 1.14281499, 1.19312592, 0.99227962, 1.15012638,\n",
       "       0.9491481 , 0.78790809, 0.85360006, 1.02676137, 0.78269215,\n",
       "       0.96362185, 1.0320481 , 1.0686262 , 1.02623744, 1.03100012,\n",
       "       0.73467768, 0.83663241, 0.97695723, 1.06613588, 1.2249729 ,\n",
       "       0.85987283, 1.00761228, 1.01508609, 0.89311399, 0.94658336,\n",
       "       0.99668099, 0.78303247, 1.0743655 , 0.78096666, 0.91858918])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00d204d2-9720-4413-a82b-b6f42ad923a4' class='xr-section-summary-in' type='checkbox'  ><label for='section-00d204d2-9720-4413-a82b-b6f42ad923a4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd6b8423-3958-49e1-80b6-89d255ebe14e' class='xr-index-data-in' type='checkbox'/><label for='index-cd6b8423-3958-49e1-80b6-89d255ebe14e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26b91a4c-d819-4778-86c0-59aeb5168f7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26b91a4c-d819-4778-86c0-59aeb5168f7f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:20.241424</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> sample_stats_prior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stan_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e161f592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.977</td>\n",
       "      <td>1.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4235.0</td>\n",
       "      <td>2967.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3764.0</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.138</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3197.0</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  1.000  0.011   0.977    1.018      0.000    0.000    4235.0    2967.0   \n",
       "beta   0.004  0.056  -0.101    0.106      0.001    0.001    3764.0    3240.0   \n",
       "sigma  0.138  0.008   0.125    0.152      0.000    0.000    3197.0    2737.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  \n",
       "beta     1.0  \n",
       "sigma    1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(stan_data, var_names=['alpha', 'beta', 'sigma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa64444",
   "metadata": {},
   "source": [
    "### R Code 8.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48e2f589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 8))\n",
    "\n",
    "alpha = np.random.normal(1, 1, 1000)\n",
    "beta = np.random.normal(0, 1, 1000)\n",
    "\n",
    "rugged_seq = np.linspace(0, 1, 100)\n",
    "\n",
    "for i in range(100):\n",
    "    plt.plot(rugged_seq, alpha[i] + beta[i] * rugged_seq, c='blue')\n",
    "    \n",
    "plt.axhline(y=1.3, c='r', ls='--')    \n",
    "plt.axhline(y=0.7, c='r', ls='--')    \n",
    "    \n",
    "plt.ylim((0.5, 1.5))\n",
    "plt.title('Using vague priors')\n",
    "plt.xlabel('Ruggedness')\n",
    "plt.ylabel('Log GDP')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38439b20",
   "metadata": {},
   "source": [
    "### R Code 8.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd46f215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 8))\n",
    "\n",
    "plt.hist(beta, bins=30, rwidth=0.9)\n",
    "plt.axvline(x=0.6, c='r', ls='--')\n",
    "plt.axvline(x=-0.6, c='r', ls='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb980eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.525"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.sum(np.abs(beta) > 0.6) / len(beta))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22780013",
   "metadata": {},
   "source": [
    "### R Code 8.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "90f575ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 8))\n",
    "\n",
    "alpha = np.random.normal(1, 0.1, 1000)\n",
    "beta = np.random.normal(0, 0.3, 1000)\n",
    "\n",
    "rugged_seq = np.linspace(0, 1, 100)\n",
    "\n",
    "for i in range(100):\n",
    "    plt.plot(rugged_seq, alpha[i] + beta[i] * rugged_seq, c='blue')\n",
    "    \n",
    "plt.axhline(y=1.3, c='r', ls='--')    \n",
    "plt.axhline(y=0.7, c='r', ls='--')    \n",
    "\n",
    "plt.ylim((0.5, 1.5))\n",
    "plt.title('Using a informative prior')\n",
    "plt.xlabel('Ruggedness')\n",
    "plt.ylabel('Log GDP')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fedde334",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 2.9e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 6.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.68 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_6a_a0a_z/model_wh2htuhu.stan', line 30, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 6.9e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.69 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_6a_a0a_z/model_wh2htuhu.stan', line 30, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model2 = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] log_gdp_std;\n",
    "        vector[N] rugged_std;\n",
    "        real rugged_std_average;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta;\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta * (rugged_std - rugged_std_average);\n",
    "    \n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        \n",
    "        alpha ~ normal(1, 0.1);\n",
    "        beta ~ normal(0, 0.3);\n",
    "        sigma ~ exponential(1);\n",
    "        \n",
    "        // Likelihood\n",
    "        log_gdp_std ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;  // By default, if a variable log_lik is present in the Stan model, it will be retrieved as pointwise log likelihood values.\n",
    "        vector[N] log_gdp_std_hat;\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
    "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(ddf),\n",
    "    'log_gdp_std': ddf['log_gdp_std'].values,\n",
    "    'rugged_std': ddf['rugged_std'].values,\n",
    "    'rugged_std_average': ddf['rugged_std'].mean(),\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model2, data=data)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab469343",
   "metadata": {},
   "outputs": [],
   "source": [
    "stan_data2 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_predictive=\"log_gdp_std_hat\",\n",
    "    observed_data=['log_gdp_std'],\n",
    "    prior=samples,\n",
    "    prior_model=posteriori,\n",
    "    posterior_model=posteriori,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b3b165d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.977</td>\n",
       "      <td>1.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4235.0</td>\n",
       "      <td>2967.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.101</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3764.0</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.138</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3197.0</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha  1.000  0.011   0.977    1.018      0.000    0.000    4235.0    2967.0   \n",
       "beta   0.004  0.056  -0.101    0.106      0.001    0.001    3764.0    3240.0   \n",
       "sigma  0.138  0.008   0.125    0.152      0.000    0.000    3197.0    2737.0   \n",
       "\n",
       "       r_hat  \n",
       "alpha    1.0  \n",
       "beta     1.0  \n",
       "sigma    1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(stan_data, var_names=['alpha', 'beta', 'sigma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53054c82",
   "metadata": {},
   "source": [
    "### R Code 8.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e33f5646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cont_africa</th>\n",
       "      <th>cid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cont_africa  cid\n",
       "2            1    1\n",
       "4            0    2\n",
       "7            0    2\n",
       "8            0    2\n",
       "9            0    2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf['cid'] = [1 if cont_africa == 1 else 2 for cont_africa in ddf['cont_africa']]\n",
    "ddf[['cont_africa', 'cid']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb817c98",
   "metadata": {},
   "source": [
    "### R Code 8.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9bd7eccf",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan_fy40ztvq/model_22jq2cue.stan', line 11, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 2.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_gl43s4d2/model_22jq2cue.stan', line 32, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 1.5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_gl43s4d2/model_22jq2cue.stan', line 32, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 3.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model3 = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] log_gdp_std;\n",
    "        vector[N] rugged_std;\n",
    "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
    "        real rugged_std_average;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
    "        real beta;\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            mu[i] = alpha[ cid[i] ] + beta * (rugged_std[i] - rugged_std_average);\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        \n",
    "        alpha ~ normal(1, 0.1);\n",
    "        beta ~ normal(0, 0.3);\n",
    "        sigma ~ exponential(1);\n",
    "        \n",
    "        // Likelihood\n",
    "        log_gdp_std ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;\n",
    "        vector[N] log_gdp_std_hat;\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
    "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(ddf),\n",
    "    'log_gdp_std': ddf['log_gdp_std'].values,\n",
    "    'rugged_std': ddf['rugged_std'].values,\n",
    "    'rugged_std_average': ddf['rugged_std'].mean(),\n",
    "    'cid': ddf['cid'].values,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model3, data=data)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e925fa34",
   "metadata": {},
   "outputs": [],
   "source": [
    "stan_data3 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_predictive=\"log_gdp_std_hat\",\n",
    "    observed_data=['log_gdp_std'],\n",
    "    prior=samples,\n",
    "    prior_model=posteriori,\n",
    "    posterior_model=posteriori,\n",
    "    dims={\n",
    "        \"alpha\": [\"africa\"],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b5eaa8",
   "metadata": {},
   "source": [
    "### R Code 8.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "afc2a79f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rodolpho/Projects/bayesian/BAYES/lib/python3.8/site-packages/arviz/stats/stats.py:1645: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
      "See http://arxiv.org/abs/1507.04544 for details\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>elpd_waic</th>\n",
       "      <th>p_waic</th>\n",
       "      <th>elpd_diff</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m8.2</th>\n",
       "      <td>0</td>\n",
       "      <td>126.166124</td>\n",
       "      <td>4.117261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.969256</td>\n",
       "      <td>7.396644</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m8.1</th>\n",
       "      <td>1</td>\n",
       "      <td>94.499399</td>\n",
       "      <td>2.491680</td>\n",
       "      <td>31.666725</td>\n",
       "      <td>0.030744</td>\n",
       "      <td>6.463956</td>\n",
       "      <td>7.317875</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rank   elpd_waic    p_waic  elpd_diff    weight        se       dse  \\\n",
       "m8.2     0  126.166124  4.117261   0.000000  0.969256  7.396644  0.000000   \n",
       "m8.1     1   94.499399  2.491680  31.666725  0.030744  6.463956  7.317875   \n",
       "\n",
       "      warning scale  \n",
       "m8.2     True   log  \n",
       "m8.1    False   log  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_8 = { 'm8.1': stan_data2, 'm8.2': stan_data3 }\n",
    "\n",
    "az.compare(models_8, ic='waic')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84517067",
   "metadata": {},
   "source": [
    "### R Code 8.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9c0836a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>0.880</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4512.0</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>1.049</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.031</td>\n",
       "      <td>1.069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4305.0</td>\n",
       "      <td>2642.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.046</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3508.0</td>\n",
       "      <td>2517.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.114</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3742.0</td>\n",
       "      <td>3076.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0]  0.880  0.016   0.851    0.911      0.000    0.000    4512.0   \n",
       "alpha[1]  1.049  0.010   1.031    1.069      0.000    0.000    4305.0   \n",
       "beta     -0.046  0.047  -0.130    0.048      0.001    0.001    3508.0   \n",
       "sigma     0.114  0.006   0.103    0.126      0.000    0.000    3742.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    3483.0    1.0  \n",
       "alpha[1]    2642.0    1.0  \n",
       "beta        2517.0    1.0  \n",
       "sigma       3076.0    1.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(stan_data3, var_names=['alpha', 'beta', 'sigma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe15bba",
   "metadata": {},
   "source": [
    "### R Code 8.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1a9aa",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (chain: 4, draw: 1000, africa: 2, mu_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 0 1 2 3\n",
       "  * draw      (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * africa    (africa) int64 0 1\n",
       "  * mu_dim_0  (mu_dim_0) int64 0 1 2 3 4 5 6 7 ... 163 164 165 166 167 168 169\n",
       "Data variables:\n",
       "    alpha     (chain, draw, africa) float64 0.8731 1.052 0.8843 ... 0.8777 1.072\n",
       "    beta      (chain, draw) float64 -0.04755 -0.02791 ... -0.07995 0.03227\n",
       "    sigma     (chain, draw) float64 0.1152 0.1109 0.11 ... 0.1056 0.1162 0.1162\n",
       "    mu        (chain, draw, mu_dim_0) float64 0.8768 1.036 ... 0.8736 0.877\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.122181\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/22jq2cue\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b1521239-7646-4505-8581-8e75465d8c7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1521239-7646-4505-8581-8e75465d8c7f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>africa</span>: 2</li><li><span class='xr-has-index'>mu_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-364ae663-d4d0-44f5-8559-6e72ea0b83ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-364ae663-d4d0-44f5-8559-6e72ea0b83ac' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-5cb65eb8-029e-4033-a625-9f5c33c71ccd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cb65eb8-029e-4033-a625-9f5c33c71ccd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45331d20-409b-4469-a696-d086bbff18fb' class='xr-var-data-in' type='checkbox'><label for='data-45331d20-409b-4469-a696-d086bbff18fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-98a88f89-2dca-4aa7-bae1-12cdddd90401' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98a88f89-2dca-4aa7-bae1-12cdddd90401' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1499cc71-1c63-4796-ab69-c49cbb2fc9c9' class='xr-var-data-in' type='checkbox'><label for='data-1499cc71-1c63-4796-ab69-c49cbb2fc9c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>africa</span></div><div class='xr-var-dims'>(africa)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-ee27b135-916c-49a5-8ece-f5abb274b62b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee27b135-916c-49a5-8ece-f5abb274b62b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d13ae87-9f1f-462a-9b88-4ea7055756dc' class='xr-var-data-in' type='checkbox'><label for='data-1d13ae87-9f1f-462a-9b88-4ea7055756dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-aaa9e3f6-4619-4da1-b62b-077b0b64d8c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaa9e3f6-4619-4da1-b62b-077b0b64d8c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0bb58c-fc9d-4db9-a004-10a3a76a1a94' class='xr-var-data-in' type='checkbox'><label for='data-3c0bb58c-fc9d-4db9-a004-10a3a76a1a94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21cb7124-8118-4770-b0dc-7c3dfc0450dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21cb7124-8118-4770-b0dc-7c3dfc0450dd' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw, africa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8731 1.052 ... 0.8777 1.072</div><input id='attrs-969fac83-9024-4ddc-ba39-8da406690b07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-969fac83-9024-4ddc-ba39-8da406690b07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4080d572-0b3a-4030-943f-04f4b719aa8f' class='xr-var-data-in' type='checkbox'><label for='data-4080d572-0b3a-4030-943f-04f4b719aa8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.87313645, 1.05199066],\n",
       "        [0.88427057, 1.0507934 ],\n",
       "        [0.89158732, 1.05320185],\n",
       "        ...,\n",
       "        [0.87292139, 1.05611766],\n",
       "        [0.8862299 , 1.06841386],\n",
       "        [0.8881574 , 1.05288457]],\n",
       "\n",
       "       [[0.89420393, 1.06698832],\n",
       "        [0.85230572, 1.03805692],\n",
       "        [0.84699101, 1.05171787],\n",
       "        ...,\n",
       "        [0.88148222, 1.05885014],\n",
       "        [0.88811556, 1.04414765],\n",
       "        [0.88879678, 1.03848589]],\n",
       "\n",
       "       [[0.89043481, 1.05997038],\n",
       "        [0.89118026, 1.04732952],\n",
       "        [0.88240801, 1.03947666],\n",
       "        ...,\n",
       "        [0.88897367, 1.04469191],\n",
       "        [0.88922894, 1.04590925],\n",
       "        [0.91395129, 1.0534571 ]],\n",
       "\n",
       "       [[0.86950711, 1.04231087],\n",
       "        [0.87466232, 1.05468656],\n",
       "        [0.8726419 , 1.05042876],\n",
       "        ...,\n",
       "        [0.89455941, 1.06780619],\n",
       "        [0.84606892, 1.02444794],\n",
       "        [0.87774696, 1.07182241]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.04755 -0.02791 ... 0.03227</div><input id='attrs-fc1ed126-fd81-4b41-9052-5fc9c2d58e6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc1ed126-fd81-4b41-9052-5fc9c2d58e6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1175816c-faae-47d3-bff4-539d2468bb7d' class='xr-var-data-in' type='checkbox'><label for='data-1175816c-faae-47d3-bff4-539d2468bb7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.04754908, -0.02791207, -0.02631636, ..., -0.06959058,\n",
       "        -0.03397767, -0.01316513],\n",
       "       [ 0.01857397, -0.07451263, -0.08199575, ..., -0.01107539,\n",
       "        -0.04876618, -0.0599864 ],\n",
       "       [-0.10553703, -0.04851136, -0.06467414, ...,  0.00669864,\n",
       "         0.0263532 , -0.03656504],\n",
       "       [-0.11949631, -0.04426819, -0.09557383, ..., -0.04794869,\n",
       "        -0.07995231,  0.03227306]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1152 0.1109 ... 0.1162 0.1162</div><input id='attrs-7a5692ee-fbe4-4633-bf1f-9b7fed46c4b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a5692ee-fbe4-4633-bf1f-9b7fed46c4b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b2257cb-d61c-4b8b-b4ab-1502a26ac70a' class='xr-var-data-in' type='checkbox'><label for='data-4b2257cb-d61c-4b8b-b4ab-1502a26ac70a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.11518267, 0.11085406, 0.10999675, ..., 0.11998923, 0.11966565,\n",
       "        0.12026951],\n",
       "       [0.11773067, 0.11447696, 0.11703635, ..., 0.09787827, 0.11234012,\n",
       "        0.10906814],\n",
       "       [0.10070415, 0.11068833, 0.11544072, ..., 0.11300423, 0.11295548,\n",
       "        0.12134894],\n",
       "       [0.11423373, 0.11127551, 0.11797346, ..., 0.10561723, 0.11623001,\n",
       "        0.11621052]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8768 1.036 1.056 ... 0.8736 0.877</div><input id='attrs-61f85b16-059b-43dc-abd4-1f8269741bec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61f85b16-059b-43dc-abd4-1f8269741bec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46bd6136-bf17-4063-b1e7-ec62da2ec3d8' class='xr-var-data-in' type='checkbox'><label for='data-46bd6136-bf17-4063-b1e7-ec62da2ec3d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.87677955, 1.03593792, 1.05631609, ..., 0.86985649,\n",
       "         0.87927124, 0.87420353],\n",
       "        [0.88640912, 1.04137019, 1.0533325 , ..., 0.88234518,\n",
       "         0.88787178, 0.88489696],\n",
       "        [0.89360362, 1.04431735, 1.05559579, ..., 0.889772  ,\n",
       "         0.89498266, 0.8921779 ],\n",
       "        ...,\n",
       "        [0.87825326, 1.03262363, 1.06244817, ..., 0.86812099,\n",
       "         0.88189997, 0.87448311],\n",
       "        [0.88883319, 1.05694287, 1.07150473, ..., 0.8838861 ,\n",
       "         0.8906137 , 0.88699241],\n",
       "        [0.88916608, 1.04843997, 1.05408217, ..., 0.88724926,\n",
       "         0.88985596, 0.88845284]],\n",
       "\n",
       "       [[0.89278083, 1.07325896, 1.06529868, ..., 0.89548517,\n",
       "         0.89180751, 0.8937871 ],\n",
       "        [0.8580147 , 1.01290118, 1.04483517, ..., 0.84716579,\n",
       "         0.86191934, 0.85397789],\n",
       "        [0.85327333, 1.02403581, 1.05917685, ..., 0.8413349 ,\n",
       "         0.85757011, 0.84883112],\n",
       "...\n",
       "        [0.88846043, 1.0469534 , 1.04408255, ..., 0.88943574,\n",
       "         0.88810941, 0.88882334],\n",
       "        [0.88720982, 1.05480619, 1.04351196, ..., 0.8910468 ,\n",
       "         0.88582885, 0.88863753],\n",
       "        [0.91675281, 1.04111261, 1.05678334, ..., 0.91142901,\n",
       "         0.91866891, 0.91477186]],\n",
       "\n",
       "       [[0.87866263, 1.00196848, 1.05318119, ..., 0.86126419,\n",
       "         0.88492453, 0.87218879],\n",
       "        [0.87805404, 1.03974145, 1.05871354, ..., 0.87160867,\n",
       "         0.8803738 , 0.87565576],\n",
       "        [0.87996454, 1.01816269, 1.0591229 , ..., 0.86604916,\n",
       "         0.88497284, 0.87478673],\n",
       "        ...,\n",
       "        [0.89823312, 1.05161853, 1.07216797, ..., 0.89125188,\n",
       "         0.90074575, 0.89563545],\n",
       "        [0.85219467, 0.99745574, 1.03172102, ..., 0.84055376,\n",
       "         0.85638437, 0.84786317],\n",
       "        [0.87527427, 1.08271791, 1.0688866 , ..., 0.87997317,\n",
       "         0.87358309, 0.8770227 ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-99e92511-18da-4dc3-8a8b-faa781f92593' class='xr-section-summary-in' type='checkbox'  ><label for='section-99e92511-18da-4dc3-8a8b-faa781f92593' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9543d271-d606-4656-8ad4-f329863d8bcd' class='xr-index-data-in' type='checkbox'/><label for='index-9543d271-d606-4656-8ad4-f329863d8bcd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0e4e774-782b-4bd4-a523-539bdc2c41c8' class='xr-index-data-in' type='checkbox'/><label for='index-c0e4e774-782b-4bd4-a523-539bdc2c41c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>africa</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-451a3e0b-33ac-4bce-bcec-c74478c89030' class='xr-index-data-in' type='checkbox'/><label for='index-451a3e0b-33ac-4bce-bcec-c74478c89030' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;africa&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f305e5a-dc12-456a-80f9-6ea66082f03d' class='xr-index-data-in' type='checkbox'/><label for='index-8f305e5a-dc12-456a-80f9-6ea66082f03d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a660e61a-c2a3-4e55-9737-b1e09b19aa05' class='xr-section-summary-in' type='checkbox'  ><label for='section-a660e61a-c2a3-4e55-9737-b1e09b19aa05' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.122181</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/22jq2cue</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior_predictive9ddd4e38-7f34-4307-b997-0fb0d8b52b9d\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior_predictive9ddd4e38-7f34-4307-b997-0fb0d8b52b9d\" class = \"xr-section-summary\">posterior_predictive</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
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       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000, log_gdp_std_hat_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "  * log_gdp_std_hat_dim_0  (log_gdp_std_hat_dim_0) int64 0 1 2 3 ... 167 168 169\n",
       "Data variables:\n",
       "    log_gdp_std_hat        (chain, draw, log_gdp_std_hat_dim_0) float64 0.746...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.276036\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-426d13bd-8615-4207-a911-7968ba956cf0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-426d13bd-8615-4207-a911-7968ba956cf0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_gdp_std_hat_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3b2deb82-8ca0-46a0-a2a1-2ed07300af3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b2deb82-8ca0-46a0-a2a1-2ed07300af3f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-4f8b0d6b-76b8-485f-9c3d-f82af1ebc98a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f8b0d6b-76b8-485f-9c3d-f82af1ebc98a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bf7d8a-55d9-4a1d-b981-ddb7646852f4' class='xr-var-data-in' type='checkbox'><label for='data-e6bf7d8a-55d9-4a1d-b981-ddb7646852f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-a8465bd8-d208-4343-8961-6ad1c8a9d506' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8465bd8-d208-4343-8961-6ad1c8a9d506' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea3072e-2cbb-4d94-951d-53b3ab421499' class='xr-var-data-in' type='checkbox'><label for='data-2ea3072e-2cbb-4d94-951d-53b3ab421499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_hat_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-f1a00d99-9e69-40d6-875f-84aa3f757278' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1a00d99-9e69-40d6-875f-84aa3f757278' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2e96e72-f0f9-459e-9b60-e9e65e18337c' class='xr-var-data-in' type='checkbox'><label for='data-e2e96e72-f0f9-459e-9b60-e9e65e18337c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da1661f0-7e31-4a04-9def-85c3a969bd4b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da1661f0-7e31-4a04-9def-85c3a969bd4b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std_hat</span></div><div class='xr-var-dims'>(chain, draw, log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7466 1.014 1.001 ... 0.7791 0.732</div><input id='attrs-f8916e56-2150-4658-baa5-1b0609d59f0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8916e56-2150-4658-baa5-1b0609d59f0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8a91a8e-ba00-49f8-8219-7277fc219f4e' class='xr-var-data-in' type='checkbox'><label for='data-b8a91a8e-ba00-49f8-8219-7277fc219f4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.74664026, 1.01425439, 1.00066688, ..., 0.7627656 ,\n",
       "         0.91098886, 0.88671518],\n",
       "        [0.94935004, 1.05391617, 1.00358255, ..., 0.82117219,\n",
       "         0.83815369, 0.70804612],\n",
       "        [0.98512799, 1.03026419, 0.98331031, ..., 0.964003  ,\n",
       "         0.79340091, 1.01635835],\n",
       "        ...,\n",
       "        [0.90852444, 0.91415448, 1.00288579, ..., 1.12913679,\n",
       "         1.08497713, 0.79824394],\n",
       "        [0.90232787, 0.97018514, 1.12605194, ..., 0.8007267 ,\n",
       "         0.85725611, 0.80667777],\n",
       "        [1.05314392, 1.07118055, 1.18655058, ..., 0.87421464,\n",
       "         0.80770721, 0.79572183]],\n",
       "\n",
       "       [[0.87772958, 1.18608474, 1.04376457, ..., 0.98590313,\n",
       "         1.06340796, 0.88084936],\n",
       "        [0.97867788, 0.81616651, 1.00465797, ..., 0.75774282,\n",
       "         0.87212006, 0.74733794],\n",
       "        [0.84362079, 0.95635079, 1.07529963, ..., 0.88726795,\n",
       "         0.8877241 , 0.83501291],\n",
       "...\n",
       "        [1.19647982, 1.00165087, 1.14573348, ..., 0.94747116,\n",
       "         0.87650929, 0.94199127],\n",
       "        [0.82385899, 0.95221412, 1.16501827, ..., 0.93369224,\n",
       "         0.8029332 , 1.00006277],\n",
       "        [1.17568388, 1.16094123, 0.90242976, ..., 0.76817132,\n",
       "         0.9884087 , 0.90550139]],\n",
       "\n",
       "       [[0.98992559, 0.94735299, 1.16526348, ..., 0.79930055,\n",
       "         0.76183148, 0.95129728],\n",
       "        [0.93608544, 1.34290369, 1.04522377, ..., 0.91450881,\n",
       "         0.91593443, 0.78917781],\n",
       "        [1.07004625, 1.13857543, 1.10689522, ..., 0.84483912,\n",
       "         0.8421724 , 0.83385613],\n",
       "        ...,\n",
       "        [0.9630556 , 1.16647952, 0.93853602, ..., 0.85020809,\n",
       "         0.7947427 , 0.95850475],\n",
       "        [0.83867249, 1.00574419, 1.02898907, ..., 1.01629985,\n",
       "         0.77960453, 0.9784706 ],\n",
       "        [0.94977879, 1.16860642, 1.14620221, ..., 0.79950032,\n",
       "         0.77911144, 0.73199524]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1849345d-e9a3-4829-956f-996b5d4fff65' class='xr-section-summary-in' type='checkbox'  ><label for='section-1849345d-e9a3-4829-956f-996b5d4fff65' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a2cafae-39d5-44fe-bc45-068bea3b31ab' class='xr-index-data-in' type='checkbox'/><label for='index-5a2cafae-39d5-44fe-bc45-068bea3b31ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2414b703-ed1e-4343-9e72-b89a52bbe071' class='xr-index-data-in' type='checkbox'/><label for='index-2414b703-ed1e-4343-9e72-b89a52bbe071' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60fbe0e6-03c8-452a-b239-24bbfc725509' class='xr-index-data-in' type='checkbox'/><label for='index-60fbe0e6-03c8-452a-b239-24bbfc725509' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_hat_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0cab5ba1-7f15-48fd-bb66-0f90206b77d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0cab5ba1-7f15-48fd-bb66-0f90206b77d7' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.276036</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (chain: 4, draw: 1000, log_lik_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain          (chain) int64 0 1 2 3\n",
       "  * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_lik_dim_0  (log_lik_dim_0) int64 0 1 2 3 4 5 ... 164 165 166 167 168 169\n",
       "Data variables:\n",
       "    log_lik        (chain, draw, log_lik_dim_0) float64 1.242 1.051 ... 1.169\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.225035\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f89d0c08-40f3-4e9b-97af-79c979c1750d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f89d0c08-40f3-4e9b-97af-79c979c1750d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2004f478-1615-4699-b440-6b98c2dac5eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2004f478-1615-4699-b440-6b98c2dac5eb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-74747cca-00c0-4805-bdd9-1cb5d39dc87a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74747cca-00c0-4805-bdd9-1cb5d39dc87a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e893ee2f-9405-4d2b-8025-7f300e147bf4' class='xr-var-data-in' type='checkbox'><label for='data-e893ee2f-9405-4d2b-8025-7f300e147bf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-8b40b343-632e-4da6-8043-0b37b8a7244d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b40b343-632e-4da6-8043-0b37b8a7244d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03b305e5-3b1d-466d-80b9-f77daa14788c' class='xr-var-data-in' type='checkbox'><label for='data-03b305e5-3b1d-466d-80b9-f77daa14788c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-0a4d7f59-cfdc-4476-92ea-cdf0a9160254' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a4d7f59-cfdc-4476-92ea-cdf0a9160254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b4a4e96-7370-4f31-8cfc-a02044eafc15' class='xr-var-data-in' type='checkbox'><label for='data-0b4a4e96-7370-4f31-8cfc-a02044eafc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-498fb27f-05f4-44bf-a53e-78dbc5000133' class='xr-section-summary-in' type='checkbox'  checked><label for='section-498fb27f-05f4-44bf-a53e-78dbc5000133' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.242 1.051 0.7867 ... 0.9158 1.169</div><input id='attrs-bb51f405-3260-40fd-9ad0-83bc96d5ba81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb51f405-3260-40fd-9ad0-83bc96d5ba81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5559d312-d3ed-4663-bcab-e42c451f2e37' class='xr-var-data-in' type='checkbox'><label for='data-5559d312-d3ed-4663-bcab-e42c451f2e37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.24197338,  1.05133367,  0.78665836, ..., -0.33393621,\n",
       "          0.87809469,  1.16805009],\n",
       "        [ 1.27877722,  1.04176611,  0.76162658, ..., -0.21963939,\n",
       "          0.81559053,  1.23441453],\n",
       "        [ 1.28039103,  1.02677129,  0.78218353, ..., -0.11976511,\n",
       "          0.75115864,  1.2595396 ],\n",
       "        ...,\n",
       "        [ 1.20134083,  1.04144879,  0.82707406, ..., -0.27582238,\n",
       "          0.84761743,  1.13385589],\n",
       "        [ 1.20121011,  0.90737149,  0.89054552, ..., -0.06274007,\n",
       "          0.78433214,  1.16925601],\n",
       "        [ 1.19599192,  0.95700263,  0.76401651, ..., -0.01118729,\n",
       "          0.78922744,  1.16768806]],\n",
       "\n",
       "       [[ 1.21425588,  0.79571515,  0.85263466, ...,  0.0661245 ,\n",
       "          0.77722666,  1.19822669],\n",
       "        [ 1.23048179,  1.16000028,  0.68581178, ..., -0.72102685,\n",
       "          0.99841078,  1.08916709],\n",
       "        [ 1.20081676,  1.09805154,  0.80767753, ..., -0.75589939,\n",
       "          1.01212941,  1.04870184],\n",
       "...\n",
       "        [ 1.25839473,  0.99683985,  0.67682041, ..., -0.07765049,\n",
       "          0.81191584,  1.2267005 ],\n",
       "        [ 1.25961983,  0.9440352 ,  0.67127007, ..., -0.05512591,\n",
       "          0.8309064 ,  1.22666718],\n",
       "        [ 1.14355985,  0.99217348,  0.78311849, ...,  0.28871086,\n",
       "          0.54630335,  1.18965179]],\n",
       "\n",
       "       [[ 1.25052798,  1.1975075 ,  0.76053818, ..., -0.48944713,\n",
       "          0.8364789 ,  1.16807575],\n",
       "        [ 1.27669654,  1.04974789,  0.80966667, ..., -0.38324474,\n",
       "          0.87777713,  1.20237518],\n",
       "        [ 1.21835479,  1.11588303,  0.80591189, ..., -0.34064982,\n",
       "          0.82974196,  1.14942867],\n",
       "        ...,\n",
       "        [ 1.31361928,  0.99079143,  0.93207521, ..., -0.17394512,\n",
       "          0.68591897,  1.30537917],\n",
       "        [ 1.20522089,  1.19366737,  0.56321071, ..., -0.79008123,\n",
       "          1.02273208,  1.04810926],\n",
       "        [ 1.23268426,  0.71821762,  0.88229508, ..., -0.16564981,\n",
       "          0.91583189,  1.16944493]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfd3945d-2e00-4e68-917b-2b98c17c4a89' class='xr-section-summary-in' type='checkbox'  ><label for='section-bfd3945d-2e00-4e68-917b-2b98c17c4a89' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54c6f182-4c03-435f-a3df-a0d2b9786290' class='xr-index-data-in' type='checkbox'/><label for='index-54c6f182-4c03-435f-a3df-a0d2b9786290' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3e8d761-a8f0-4768-bd04-6e5fdf7bdeee' class='xr-index-data-in' type='checkbox'/><label for='index-b3e8d761-a8f0-4768-bd04-6e5fdf7bdeee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65ea1ff1-f5c7-418e-bbc6-1522652018c7' class='xr-index-data-in' type='checkbox'/><label for='index-65ea1ff1-f5c7-418e-bbc6-1522652018c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b0001a1-6219-43f0-9262-ee734a2df913' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b0001a1-6219-43f0-9262-ee734a2df913' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.225035</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    acceptance_rate  (chain, draw) float64 0.9829 0.9857 0.9661 ... 0.7619 1.0\n",
       "    step_size        (chain, draw) float64 0.7249 0.7249 ... 0.7518 0.7518\n",
       "    tree_depth       (chain, draw) int64 3 3 2 2 3 2 3 2 3 ... 3 2 3 2 3 3 3 3 3\n",
       "    n_steps          (chain, draw) int64 7 7 3 7 7 3 7 3 7 ... 7 3 7 3 7 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -281.1 -282.8 ... -274.9 -275.5\n",
       "    lp               (chain, draw) float64 283.2 283.2 282.8 ... 278.0 279.2\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.172412\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/22jq2cue\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d12d92b5-a87c-420e-8505-2b95773fced1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d12d92b5-a87c-420e-8505-2b95773fced1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff8ee196-045c-48e4-a18c-1e7e4e12faab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff8ee196-045c-48e4-a18c-1e7e4e12faab' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-d15802f9-88e8-45c1-9685-11af87c2455a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d15802f9-88e8-45c1-9685-11af87c2455a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed11514-f1f2-43d6-b39c-624d494c116f' class='xr-var-data-in' type='checkbox'><label for='data-aed11514-f1f2-43d6-b39c-624d494c116f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-57b76373-0e5d-48dd-b00f-28bb7dc3e315' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57b76373-0e5d-48dd-b00f-28bb7dc3e315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2709e86f-5aff-43b6-a9a5-76a8a1a64d0c' class='xr-var-data-in' type='checkbox'><label for='data-2709e86f-5aff-43b6-a9a5-76a8a1a64d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-608c41e3-05c9-4ba5-be53-0a3b9b52200b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-608c41e3-05c9-4ba5-be53-0a3b9b52200b' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9829 0.9857 0.9661 ... 0.7619 1.0</div><input id='attrs-b09cc869-cdcf-4cd5-95dc-00d9b821eef1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b09cc869-cdcf-4cd5-95dc-00d9b821eef1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a72102ee-eee6-460a-906c-988d2058dfb3' class='xr-var-data-in' type='checkbox'><label for='data-a72102ee-eee6-460a-906c-988d2058dfb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.98285519, 0.98565226, 0.96613038, ..., 0.96671227, 0.92624128,\n",
       "        0.96580296],\n",
       "       [1.        , 0.99044508, 0.99433735, ..., 0.79785273, 1.        ,\n",
       "        0.92410778],\n",
       "       [0.57212955, 0.97274345, 0.96641087, ..., 0.93861901, 0.97291309,\n",
       "        0.65305686],\n",
       "       [0.94411824, 0.86059722, 0.92331214, ..., 0.82749648, 0.76191425,\n",
       "        1.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7249 0.7249 ... 0.7518 0.7518</div><input id='attrs-a9bc1230-bcaa-42f5-b0a7-57e56eeb719c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9bc1230-bcaa-42f5-b0a7-57e56eeb719c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03a994bc-1d7c-4236-adbf-de7069a739b9' class='xr-var-data-in' type='checkbox'><label for='data-03a994bc-1d7c-4236-adbf-de7069a739b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.72489257, 0.72489257, 0.72489257, ..., 0.72489257, 0.72489257,\n",
       "        0.72489257],\n",
       "       [0.69165873, 0.69165873, 0.69165873, ..., 0.69165873, 0.69165873,\n",
       "        0.69165873],\n",
       "       [0.77068046, 0.77068046, 0.77068046, ..., 0.77068046, 0.77068046,\n",
       "        0.77068046],\n",
       "       [0.75183437, 0.75183437, 0.75183437, ..., 0.75183437, 0.75183437,\n",
       "        0.75183437]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 2 2 3 2 3 2 ... 2 3 2 3 3 3 3 3</div><input id='attrs-4d445137-c2c1-4ec1-9dff-38320ecb640b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d445137-c2c1-4ec1-9dff-38320ecb640b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbb01b49-2873-4e66-84ce-8404a0eb4454' class='xr-var-data-in' type='checkbox'><label for='data-fbb01b49-2873-4e66-84ce-8404a0eb4454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 2, ..., 3, 2, 2],\n",
       "       [2, 3, 3, ..., 2, 2, 2],\n",
       "       [1, 2, 2, ..., 2, 2, 2],\n",
       "       [3, 2, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>7 7 3 7 7 3 7 3 ... 3 7 3 7 7 7 7 7</div><input id='attrs-3a96b1d5-06a3-40b2-b79e-04363978bfae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a96b1d5-06a3-40b2-b79e-04363978bfae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d780671-dcb0-4f59-88ad-631c52aacd0e' class='xr-var-data-in' type='checkbox'><label for='data-7d780671-dcb0-4f59-88ad-631c52aacd0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7, 7, 3, ..., 7, 7, 3],\n",
       "       [3, 7, 7, ..., 3, 3, 3],\n",
       "       [1, 3, 7, ..., 3, 3, 3],\n",
       "       [7, 3, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9197420f-e734-4814-b80c-ad7d59ce85ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9197420f-e734-4814-b80c-ad7d59ce85ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45a39f61-2a8e-4ad9-a68b-5a5ad9bbdf84' class='xr-var-data-in' type='checkbox'><label for='data-45a39f61-2a8e-4ad9-a68b-5a5ad9bbdf84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-281.1 -282.8 ... -274.9 -275.5</div><input id='attrs-06d31534-dc18-4485-8942-16814295a239' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06d31534-dc18-4485-8942-16814295a239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29807839-0ca6-4926-a942-0b7ae102394e' class='xr-var-data-in' type='checkbox'><label for='data-29807839-0ca6-4926-a942-0b7ae102394e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-281.08899796, -282.8473195 , -282.71973987, ..., -279.74602789,\n",
       "        -280.50317191, -279.73692681],\n",
       "       [-274.27989318, -278.7327423 , -279.19631455, ..., -273.43177734,\n",
       "        -279.03922092, -282.12723298],\n",
       "       [-278.95857864, -278.16649857, -282.43529129, ..., -281.19868155,\n",
       "        -281.98194544, -277.04438463],\n",
       "       [-279.3325967 , -279.33604473, -281.83647133, ..., -277.09355835,\n",
       "        -274.90482958, -275.54699889]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>283.2 283.2 282.8 ... 278.0 279.2</div><input id='attrs-73910873-0f31-4c38-b963-eef591927331' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73910873-0f31-4c38-b963-eef591927331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76d13f7d-6483-4531-af94-7c68f5d1d736' class='xr-var-data-in' type='checkbox'><label for='data-76d13f7d-6483-4531-af94-7c68f5d1d736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[283.16240078, 283.20814155, 282.84906345, ..., 282.35381354,\n",
       "        281.09944187, 282.32844923],\n",
       "       [280.35816838, 281.11873583, 280.91347415, ..., 278.61575229,\n",
       "        283.13415067, 282.36607238],\n",
       "       [279.05930622, 283.06141184, 282.74776651, ..., 282.55710887,\n",
       "        282.02243641, 280.5697389 ],\n",
       "       [281.68408002, 283.13204044, 282.43713437, ..., 280.27036361,\n",
       "        278.02904057, 279.20730924]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf02a2f8-974d-4cbf-86ee-7625b88ef3d8' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf02a2f8-974d-4cbf-86ee-7625b88ef3d8' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2a5c518e-ac91-48d2-969b-9e1903f1a0af' class='xr-index-data-in' type='checkbox'/><label for='index-2a5c518e-ac91-48d2-969b-9e1903f1a0af' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eee9c99d-aa99-48ad-b520-2257ab113e0d' class='xr-index-data-in' type='checkbox'/><label for='index-eee9c99d-aa99-48ad-b520-2257ab113e0d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0961817-c8b7-435f-b1dc-aa275b3202d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0961817-c8b7-435f-b1dc-aa275b3202d6' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.172412</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/22jq2cue</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000, africa: 2, mu_dim_0: 170,\n",
       "                            log_lik_dim_0: 170, log_gdp_std_hat_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "  * africa                 (africa) int64 0 1\n",
       "  * mu_dim_0               (mu_dim_0) int64 0 1 2 3 4 5 ... 165 166 167 168 169\n",
       "  * log_lik_dim_0          (log_lik_dim_0) int64 0 1 2 3 4 ... 166 167 168 169\n",
       "  * log_gdp_std_hat_dim_0  (log_gdp_std_hat_dim_0) int64 0 1 2 3 ... 167 168 169\n",
       "Data variables:\n",
       "    alpha                  (chain, draw, africa) float64 0.8731 1.052 ... 1.072\n",
       "    beta                   (chain, draw) float64 -0.04755 -0.02791 ... 0.03227\n",
       "    sigma                  (chain, draw) float64 0.1152 0.1109 ... 0.1162 0.1162\n",
       "    mu                     (chain, draw, mu_dim_0) float64 0.8768 ... 0.877\n",
       "    log_lik                (chain, draw, log_lik_dim_0) float64 1.242 ... 1.169\n",
       "    log_gdp_std_hat        (chain, draw, log_gdp_std_hat_dim_0) float64 0.746...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.342979\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/22jq2cue\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-13e12b84-5ace-491f-9f04-7500f17c4c90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13e12b84-5ace-491f-9f04-7500f17c4c90' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>africa</span>: 2</li><li><span class='xr-has-index'>mu_dim_0</span>: 170</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 170</li><li><span class='xr-has-index'>log_gdp_std_hat_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-060882c7-bf6d-43e0-b053-b8480f91b198' class='xr-section-summary-in' type='checkbox'  checked><label for='section-060882c7-bf6d-43e0-b053-b8480f91b198' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-6f312cff-5633-4720-b363-79000b4d7790' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f312cff-5633-4720-b363-79000b4d7790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66c53e87-ee1f-4c11-9c40-57f44305708e' class='xr-var-data-in' type='checkbox'><label for='data-66c53e87-ee1f-4c11-9c40-57f44305708e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-8562a6bf-52e4-42eb-a715-2fd8ecb41e3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8562a6bf-52e4-42eb-a715-2fd8ecb41e3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e5db930-fdf9-438a-84e6-f1d887bc24a1' class='xr-var-data-in' type='checkbox'><label for='data-7e5db930-fdf9-438a-84e6-f1d887bc24a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>africa</span></div><div class='xr-var-dims'>(africa)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-26fca761-500d-49fa-b6a2-78ae3745dd0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26fca761-500d-49fa-b6a2-78ae3745dd0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bcca5b0-1eb1-4bf8-8efc-726599b29b36' class='xr-var-data-in' type='checkbox'><label for='data-1bcca5b0-1eb1-4bf8-8efc-726599b29b36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-2ff03b96-d381-48b3-a054-a5d0a24df71b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ff03b96-d381-48b3-a054-a5d0a24df71b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d147186e-064c-420a-803b-461043d2beec' class='xr-var-data-in' type='checkbox'><label for='data-d147186e-064c-420a-803b-461043d2beec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-2ce3f2ec-f53c-41ca-a099-1ff335108c1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ce3f2ec-f53c-41ca-a099-1ff335108c1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2007b8f-b646-4206-b463-9b6b9639b133' class='xr-var-data-in' type='checkbox'><label for='data-f2007b8f-b646-4206-b463-9b6b9639b133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_hat_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-466217bc-ab05-4e50-b661-29536b9619da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-466217bc-ab05-4e50-b661-29536b9619da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fc85127-c0e5-4baa-88f0-1e3cc450ebde' class='xr-var-data-in' type='checkbox'><label for='data-1fc85127-c0e5-4baa-88f0-1e3cc450ebde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15ad0095-c77e-44c3-87d4-e2f246c680c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15ad0095-c77e-44c3-87d4-e2f246c680c2' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw, africa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8731 1.052 ... 0.8777 1.072</div><input id='attrs-6e3f99d5-b049-468a-a2da-33708363cc27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e3f99d5-b049-468a-a2da-33708363cc27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd2d2b5-7ca1-4b49-a4d7-fcfbc4e14235' class='xr-var-data-in' type='checkbox'><label for='data-6bd2d2b5-7ca1-4b49-a4d7-fcfbc4e14235' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.87313645, 1.05199066],\n",
       "        [0.88427057, 1.0507934 ],\n",
       "        [0.89158732, 1.05320185],\n",
       "        ...,\n",
       "        [0.87292139, 1.05611766],\n",
       "        [0.8862299 , 1.06841386],\n",
       "        [0.8881574 , 1.05288457]],\n",
       "\n",
       "       [[0.89420393, 1.06698832],\n",
       "        [0.85230572, 1.03805692],\n",
       "        [0.84699101, 1.05171787],\n",
       "        ...,\n",
       "        [0.88148222, 1.05885014],\n",
       "        [0.88811556, 1.04414765],\n",
       "        [0.88879678, 1.03848589]],\n",
       "\n",
       "       [[0.89043481, 1.05997038],\n",
       "        [0.89118026, 1.04732952],\n",
       "        [0.88240801, 1.03947666],\n",
       "        ...,\n",
       "        [0.88897367, 1.04469191],\n",
       "        [0.88922894, 1.04590925],\n",
       "        [0.91395129, 1.0534571 ]],\n",
       "\n",
       "       [[0.86950711, 1.04231087],\n",
       "        [0.87466232, 1.05468656],\n",
       "        [0.8726419 , 1.05042876],\n",
       "        ...,\n",
       "        [0.89455941, 1.06780619],\n",
       "        [0.84606892, 1.02444794],\n",
       "        [0.87774696, 1.07182241]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.04755 -0.02791 ... 0.03227</div><input id='attrs-31b98e3b-1d97-4db0-8b31-e8f1984d1f22' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31b98e3b-1d97-4db0-8b31-e8f1984d1f22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e99d1263-3301-4dcc-bef5-e66d599e2a29' class='xr-var-data-in' type='checkbox'><label for='data-e99d1263-3301-4dcc-bef5-e66d599e2a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.04754908, -0.02791207, -0.02631636, ..., -0.06959058,\n",
       "        -0.03397767, -0.01316513],\n",
       "       [ 0.01857397, -0.07451263, -0.08199575, ..., -0.01107539,\n",
       "        -0.04876618, -0.0599864 ],\n",
       "       [-0.10553703, -0.04851136, -0.06467414, ...,  0.00669864,\n",
       "         0.0263532 , -0.03656504],\n",
       "       [-0.11949631, -0.04426819, -0.09557383, ..., -0.04794869,\n",
       "        -0.07995231,  0.03227306]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1152 0.1109 ... 0.1162 0.1162</div><input id='attrs-6f12a182-6fa7-40ab-b7cb-30e2ec64609d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f12a182-6fa7-40ab-b7cb-30e2ec64609d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b27e7513-41c3-4609-bcc6-3c2c9e388816' class='xr-var-data-in' type='checkbox'><label for='data-b27e7513-41c3-4609-bcc6-3c2c9e388816' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.11518267, 0.11085406, 0.10999675, ..., 0.11998923, 0.11966565,\n",
       "        0.12026951],\n",
       "       [0.11773067, 0.11447696, 0.11703635, ..., 0.09787827, 0.11234012,\n",
       "        0.10906814],\n",
       "       [0.10070415, 0.11068833, 0.11544072, ..., 0.11300423, 0.11295548,\n",
       "        0.12134894],\n",
       "       [0.11423373, 0.11127551, 0.11797346, ..., 0.10561723, 0.11623001,\n",
       "        0.11621052]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8768 1.036 1.056 ... 0.8736 0.877</div><input id='attrs-e0ad9569-7d86-4e35-a365-17e042fd49e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0ad9569-7d86-4e35-a365-17e042fd49e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17a13fe5-ec0e-4919-a2cd-528ed27ab870' class='xr-var-data-in' type='checkbox'><label for='data-17a13fe5-ec0e-4919-a2cd-528ed27ab870' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.87677955, 1.03593792, 1.05631609, ..., 0.86985649,\n",
       "         0.87927124, 0.87420353],\n",
       "        [0.88640912, 1.04137019, 1.0533325 , ..., 0.88234518,\n",
       "         0.88787178, 0.88489696],\n",
       "        [0.89360362, 1.04431735, 1.05559579, ..., 0.889772  ,\n",
       "         0.89498266, 0.8921779 ],\n",
       "        ...,\n",
       "        [0.87825326, 1.03262363, 1.06244817, ..., 0.86812099,\n",
       "         0.88189997, 0.87448311],\n",
       "        [0.88883319, 1.05694287, 1.07150473, ..., 0.8838861 ,\n",
       "         0.8906137 , 0.88699241],\n",
       "        [0.88916608, 1.04843997, 1.05408217, ..., 0.88724926,\n",
       "         0.88985596, 0.88845284]],\n",
       "\n",
       "       [[0.89278083, 1.07325896, 1.06529868, ..., 0.89548517,\n",
       "         0.89180751, 0.8937871 ],\n",
       "        [0.8580147 , 1.01290118, 1.04483517, ..., 0.84716579,\n",
       "         0.86191934, 0.85397789],\n",
       "        [0.85327333, 1.02403581, 1.05917685, ..., 0.8413349 ,\n",
       "         0.85757011, 0.84883112],\n",
       "...\n",
       "        [0.88846043, 1.0469534 , 1.04408255, ..., 0.88943574,\n",
       "         0.88810941, 0.88882334],\n",
       "        [0.88720982, 1.05480619, 1.04351196, ..., 0.8910468 ,\n",
       "         0.88582885, 0.88863753],\n",
       "        [0.91675281, 1.04111261, 1.05678334, ..., 0.91142901,\n",
       "         0.91866891, 0.91477186]],\n",
       "\n",
       "       [[0.87866263, 1.00196848, 1.05318119, ..., 0.86126419,\n",
       "         0.88492453, 0.87218879],\n",
       "        [0.87805404, 1.03974145, 1.05871354, ..., 0.87160867,\n",
       "         0.8803738 , 0.87565576],\n",
       "        [0.87996454, 1.01816269, 1.0591229 , ..., 0.86604916,\n",
       "         0.88497284, 0.87478673],\n",
       "        ...,\n",
       "        [0.89823312, 1.05161853, 1.07216797, ..., 0.89125188,\n",
       "         0.90074575, 0.89563545],\n",
       "        [0.85219467, 0.99745574, 1.03172102, ..., 0.84055376,\n",
       "         0.85638437, 0.84786317],\n",
       "        [0.87527427, 1.08271791, 1.0688866 , ..., 0.87997317,\n",
       "         0.87358309, 0.8770227 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.242 1.051 0.7867 ... 0.9158 1.169</div><input id='attrs-637b7645-8d0e-4318-bdac-b8f1fc5930cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-637b7645-8d0e-4318-bdac-b8f1fc5930cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2981798e-9a1c-43b5-8fd6-3a8748846e7a' class='xr-var-data-in' type='checkbox'><label for='data-2981798e-9a1c-43b5-8fd6-3a8748846e7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.24197338,  1.05133367,  0.78665836, ..., -0.33393621,\n",
       "          0.87809469,  1.16805009],\n",
       "        [ 1.27877722,  1.04176611,  0.76162658, ..., -0.21963939,\n",
       "          0.81559053,  1.23441453],\n",
       "        [ 1.28039103,  1.02677129,  0.78218353, ..., -0.11976511,\n",
       "          0.75115864,  1.2595396 ],\n",
       "        ...,\n",
       "        [ 1.20134083,  1.04144879,  0.82707406, ..., -0.27582238,\n",
       "          0.84761743,  1.13385589],\n",
       "        [ 1.20121011,  0.90737149,  0.89054552, ..., -0.06274007,\n",
       "          0.78433214,  1.16925601],\n",
       "        [ 1.19599192,  0.95700263,  0.76401651, ..., -0.01118729,\n",
       "          0.78922744,  1.16768806]],\n",
       "\n",
       "       [[ 1.21425588,  0.79571515,  0.85263466, ...,  0.0661245 ,\n",
       "          0.77722666,  1.19822669],\n",
       "        [ 1.23048179,  1.16000028,  0.68581178, ..., -0.72102685,\n",
       "          0.99841078,  1.08916709],\n",
       "        [ 1.20081676,  1.09805154,  0.80767753, ..., -0.75589939,\n",
       "          1.01212941,  1.04870184],\n",
       "...\n",
       "        [ 1.25839473,  0.99683985,  0.67682041, ..., -0.07765049,\n",
       "          0.81191584,  1.2267005 ],\n",
       "        [ 1.25961983,  0.9440352 ,  0.67127007, ..., -0.05512591,\n",
       "          0.8309064 ,  1.22666718],\n",
       "        [ 1.14355985,  0.99217348,  0.78311849, ...,  0.28871086,\n",
       "          0.54630335,  1.18965179]],\n",
       "\n",
       "       [[ 1.25052798,  1.1975075 ,  0.76053818, ..., -0.48944713,\n",
       "          0.8364789 ,  1.16807575],\n",
       "        [ 1.27669654,  1.04974789,  0.80966667, ..., -0.38324474,\n",
       "          0.87777713,  1.20237518],\n",
       "        [ 1.21835479,  1.11588303,  0.80591189, ..., -0.34064982,\n",
       "          0.82974196,  1.14942867],\n",
       "        ...,\n",
       "        [ 1.31361928,  0.99079143,  0.93207521, ..., -0.17394512,\n",
       "          0.68591897,  1.30537917],\n",
       "        [ 1.20522089,  1.19366737,  0.56321071, ..., -0.79008123,\n",
       "          1.02273208,  1.04810926],\n",
       "        [ 1.23268426,  0.71821762,  0.88229508, ..., -0.16564981,\n",
       "          0.91583189,  1.16944493]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std_hat</span></div><div class='xr-var-dims'>(chain, draw, log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7466 1.014 1.001 ... 0.7791 0.732</div><input id='attrs-bb4ecc5e-4c5c-4ffe-a809-12b03fdaa64d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb4ecc5e-4c5c-4ffe-a809-12b03fdaa64d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd7727cc-283d-43d6-9caa-aafd9bb2fdb3' class='xr-var-data-in' type='checkbox'><label for='data-bd7727cc-283d-43d6-9caa-aafd9bb2fdb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.74664026, 1.01425439, 1.00066688, ..., 0.7627656 ,\n",
       "         0.91098886, 0.88671518],\n",
       "        [0.94935004, 1.05391617, 1.00358255, ..., 0.82117219,\n",
       "         0.83815369, 0.70804612],\n",
       "        [0.98512799, 1.03026419, 0.98331031, ..., 0.964003  ,\n",
       "         0.79340091, 1.01635835],\n",
       "        ...,\n",
       "        [0.90852444, 0.91415448, 1.00288579, ..., 1.12913679,\n",
       "         1.08497713, 0.79824394],\n",
       "        [0.90232787, 0.97018514, 1.12605194, ..., 0.8007267 ,\n",
       "         0.85725611, 0.80667777],\n",
       "        [1.05314392, 1.07118055, 1.18655058, ..., 0.87421464,\n",
       "         0.80770721, 0.79572183]],\n",
       "\n",
       "       [[0.87772958, 1.18608474, 1.04376457, ..., 0.98590313,\n",
       "         1.06340796, 0.88084936],\n",
       "        [0.97867788, 0.81616651, 1.00465797, ..., 0.75774282,\n",
       "         0.87212006, 0.74733794],\n",
       "        [0.84362079, 0.95635079, 1.07529963, ..., 0.88726795,\n",
       "         0.8877241 , 0.83501291],\n",
       "...\n",
       "        [1.19647982, 1.00165087, 1.14573348, ..., 0.94747116,\n",
       "         0.87650929, 0.94199127],\n",
       "        [0.82385899, 0.95221412, 1.16501827, ..., 0.93369224,\n",
       "         0.8029332 , 1.00006277],\n",
       "        [1.17568388, 1.16094123, 0.90242976, ..., 0.76817132,\n",
       "         0.9884087 , 0.90550139]],\n",
       "\n",
       "       [[0.98992559, 0.94735299, 1.16526348, ..., 0.79930055,\n",
       "         0.76183148, 0.95129728],\n",
       "        [0.93608544, 1.34290369, 1.04522377, ..., 0.91450881,\n",
       "         0.91593443, 0.78917781],\n",
       "        [1.07004625, 1.13857543, 1.10689522, ..., 0.84483912,\n",
       "         0.8421724 , 0.83385613],\n",
       "        ...,\n",
       "        [0.9630556 , 1.16647952, 0.93853602, ..., 0.85020809,\n",
       "         0.7947427 , 0.95850475],\n",
       "        [0.83867249, 1.00574419, 1.02898907, ..., 1.01629985,\n",
       "         0.77960453, 0.9784706 ],\n",
       "        [0.94977879, 1.16860642, 1.14620221, ..., 0.79950032,\n",
       "         0.77911144, 0.73199524]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-315912fc-0193-4207-8fe1-5ac2e78f1db3' class='xr-section-summary-in' type='checkbox'  ><label for='section-315912fc-0193-4207-8fe1-5ac2e78f1db3' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ce55e55-8f92-432a-9549-17667f8e5adf' class='xr-index-data-in' type='checkbox'/><label for='index-9ce55e55-8f92-432a-9549-17667f8e5adf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-94b93790-5baf-4314-a496-57f59d67240f' class='xr-index-data-in' type='checkbox'/><label for='index-94b93790-5baf-4314-a496-57f59d67240f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>africa</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-57f9df5e-7f7b-43cd-ae22-987433cc56b9' class='xr-index-data-in' type='checkbox'/><label for='index-57f9df5e-7f7b-43cd-ae22-987433cc56b9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;africa&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0ff10e78-9bd3-4f53-94f1-6a828edcf4f0' class='xr-index-data-in' type='checkbox'/><label for='index-0ff10e78-9bd3-4f53-94f1-6a828edcf4f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3be26a66-83c4-4847-966d-ab570429d40e' class='xr-index-data-in' type='checkbox'/><label for='index-3be26a66-83c4-4847-966d-ab570429d40e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7c5104f-5934-45ce-a95b-499dbe3781d5' class='xr-index-data-in' type='checkbox'/><label for='index-f7c5104f-5934-45ce-a95b-499dbe3781d5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_hat_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b4d446b-f78b-4ef6-90b8-13dff024f06f' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b4d446b-f78b-4ef6-90b8-13dff024f06f' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.342979</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/22jq2cue</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats_prior467fb472-23b8-4f37-a124-c084c8ac8be2\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats_prior467fb472-23b8-4f37-a124-c084c8ac8be2\" class = \"xr-section-summary\">sample_stats_prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
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       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 283.2 283.2 282.8 ... 278.0 279.2\n",
       "    acceptance_rate  (chain, draw) float64 0.9829 0.9857 0.9661 ... 0.7619 1.0\n",
       "    step_size        (chain, draw) float64 0.7249 0.7249 ... 0.7518 0.7518\n",
       "    tree_depth       (chain, draw) int64 3 3 2 2 3 2 3 2 3 ... 3 2 3 2 3 3 3 3 3\n",
       "    n_steps          (chain, draw) int64 7 7 3 7 7 3 7 3 7 ... 7 3 7 3 7 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -281.1 -282.8 ... -274.9 -275.5\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.392039\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/22jq2cue\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ecb4ff9c-dbc7-40e7-ad59-7906db9a028d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ecb4ff9c-dbc7-40e7-ad59-7906db9a028d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-341df5e9-bb14-4fd6-9474-bfacf6eba804' class='xr-section-summary-in' type='checkbox'  checked><label for='section-341df5e9-bb14-4fd6-9474-bfacf6eba804' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-442b63f8-b6b3-4ddb-ab37-a8c579a21a55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-442b63f8-b6b3-4ddb-ab37-a8c579a21a55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac8c13d2-402a-4df3-8c51-6aa5fbe5abf8' class='xr-var-data-in' type='checkbox'><label for='data-ac8c13d2-402a-4df3-8c51-6aa5fbe5abf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-26026192-e895-47ab-95be-3f8af91de8c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26026192-e895-47ab-95be-3f8af91de8c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b40dd8de-639c-41a2-a1ee-6c50be1281fe' class='xr-var-data-in' type='checkbox'><label for='data-b40dd8de-639c-41a2-a1ee-6c50be1281fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ff04549-4299-41b8-b825-8057794f5bda' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ff04549-4299-41b8-b825-8057794f5bda' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>283.2 283.2 282.8 ... 278.0 279.2</div><input id='attrs-dfc41d8d-9ef0-4190-bdee-c56b40b8b5e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfc41d8d-9ef0-4190-bdee-c56b40b8b5e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dca99980-89ea-4187-8dd4-f2d7108bf212' class='xr-var-data-in' type='checkbox'><label for='data-dca99980-89ea-4187-8dd4-f2d7108bf212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[283.16240078, 283.20814155, 282.84906345, ..., 282.35381354,\n",
       "        281.09944187, 282.32844923],\n",
       "       [280.35816838, 281.11873583, 280.91347415, ..., 278.61575229,\n",
       "        283.13415067, 282.36607238],\n",
       "       [279.05930622, 283.06141184, 282.74776651, ..., 282.55710887,\n",
       "        282.02243641, 280.5697389 ],\n",
       "       [281.68408002, 283.13204044, 282.43713437, ..., 280.27036361,\n",
       "        278.02904057, 279.20730924]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9829 0.9857 0.9661 ... 0.7619 1.0</div><input id='attrs-13ec9a53-11f5-4eb1-92db-846fb45f9ac4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13ec9a53-11f5-4eb1-92db-846fb45f9ac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cabdd8f-0453-4407-ad31-a2551a373d1e' class='xr-var-data-in' type='checkbox'><label for='data-8cabdd8f-0453-4407-ad31-a2551a373d1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.98285519, 0.98565226, 0.96613038, ..., 0.96671227, 0.92624128,\n",
       "        0.96580296],\n",
       "       [1.        , 0.99044508, 0.99433735, ..., 0.79785273, 1.        ,\n",
       "        0.92410778],\n",
       "       [0.57212955, 0.97274345, 0.96641087, ..., 0.93861901, 0.97291309,\n",
       "        0.65305686],\n",
       "       [0.94411824, 0.86059722, 0.92331214, ..., 0.82749648, 0.76191425,\n",
       "        1.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7249 0.7249 ... 0.7518 0.7518</div><input id='attrs-aaf2a86f-dc6a-4434-aff4-7846e9bb7bdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaf2a86f-dc6a-4434-aff4-7846e9bb7bdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c246783-9f5c-44f1-8cf2-7d1d7f19317f' class='xr-var-data-in' type='checkbox'><label for='data-2c246783-9f5c-44f1-8cf2-7d1d7f19317f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.72489257, 0.72489257, 0.72489257, ..., 0.72489257, 0.72489257,\n",
       "        0.72489257],\n",
       "       [0.69165873, 0.69165873, 0.69165873, ..., 0.69165873, 0.69165873,\n",
       "        0.69165873],\n",
       "       [0.77068046, 0.77068046, 0.77068046, ..., 0.77068046, 0.77068046,\n",
       "        0.77068046],\n",
       "       [0.75183437, 0.75183437, 0.75183437, ..., 0.75183437, 0.75183437,\n",
       "        0.75183437]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 2 2 3 2 3 2 ... 2 3 2 3 3 3 3 3</div><input id='attrs-1b235054-b37b-4847-9794-af46df4b76d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b235054-b37b-4847-9794-af46df4b76d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e89d3c55-c273-429f-8853-ef2b52738c9b' class='xr-var-data-in' type='checkbox'><label for='data-e89d3c55-c273-429f-8853-ef2b52738c9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 2, ..., 3, 2, 2],\n",
       "       [2, 3, 3, ..., 2, 2, 2],\n",
       "       [1, 2, 2, ..., 2, 2, 2],\n",
       "       [3, 2, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>7 7 3 7 7 3 7 3 ... 3 7 3 7 7 7 7 7</div><input id='attrs-e7011645-99a5-4aaa-a27c-b2aa8ac5790a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7011645-99a5-4aaa-a27c-b2aa8ac5790a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0946d80d-246e-4b62-b340-a0d1ef9d7408' class='xr-var-data-in' type='checkbox'><label for='data-0946d80d-246e-4b62-b340-a0d1ef9d7408' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7, 7, 3, ..., 7, 7, 3],\n",
       "       [3, 7, 7, ..., 3, 3, 3],\n",
       "       [1, 3, 7, ..., 3, 3, 3],\n",
       "       [7, 3, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fab27425-a8cc-4e7b-9ef2-6b3e522b0ffb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fab27425-a8cc-4e7b-9ef2-6b3e522b0ffb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-295ac424-6e3d-4501-80be-f861387ab929' class='xr-var-data-in' type='checkbox'><label for='data-295ac424-6e3d-4501-80be-f861387ab929' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-281.1 -282.8 ... -274.9 -275.5</div><input id='attrs-83c29a60-b4bb-462b-afc6-f85d47bde24d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83c29a60-b4bb-462b-afc6-f85d47bde24d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5045e9d2-304f-44bd-b3f8-29b8b8cafe03' class='xr-var-data-in' type='checkbox'><label for='data-5045e9d2-304f-44bd-b3f8-29b8b8cafe03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-281.08899796, -282.8473195 , -282.71973987, ..., -279.74602789,\n",
       "        -280.50317191, -279.73692681],\n",
       "       [-274.27989318, -278.7327423 , -279.19631455, ..., -273.43177734,\n",
       "        -279.03922092, -282.12723298],\n",
       "       [-278.95857864, -278.16649857, -282.43529129, ..., -281.19868155,\n",
       "        -281.98194544, -277.04438463],\n",
       "       [-279.3325967 , -279.33604473, -281.83647133, ..., -277.09355835,\n",
       "        -274.90482958, -275.54699889]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d18203f3-d0d5-44f6-8ed1-db6f139a6a86' class='xr-section-summary-in' type='checkbox'  ><label for='section-d18203f3-d0d5-44f6-8ed1-db6f139a6a86' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00a7a07a-b271-4bec-8412-63d9210dd4bb' class='xr-index-data-in' type='checkbox'/><label for='index-00a7a07a-b271-4bec-8412-63d9210dd4bb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca21a9a6-faf0-4a2f-a175-137fac2cc50c' class='xr-index-data-in' type='checkbox'/><label for='index-ca21a9a6-faf0-4a2f-a175-137fac2cc50c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04ef2e2b-7633-4cdf-bb8b-77624b5c94a0' class='xr-section-summary-in' type='checkbox'  ><label for='section-04ef2e2b-7633-4cdf-bb8b-77624b5c94a0' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.392039</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/22jq2cue</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (log_gdp_std_dim_0: 170)\n",
       "Coordinates:\n",
       "  * log_gdp_std_dim_0  (log_gdp_std_dim_0) int64 0 1 2 3 4 ... 166 167 168 169\n",
       "Data variables:\n",
       "    log_gdp_std        (log_gdp_std_dim_0) float64 0.8797 0.9648 ... 0.9186\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:27.082048\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0318fc78-3a23-400c-96e5-89c4f9be4342' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0318fc78-3a23-400c-96e5-89c4f9be4342' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>log_gdp_std_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7cb0d871-d9b9-43e0-8663-dd68f36e31dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7cb0d871-d9b9-43e0-8663-dd68f36e31dd' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-453f8368-ea34-4ccc-9859-63274f10e6e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-453f8368-ea34-4ccc-9859-63274f10e6e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c0154db-57a0-46c3-b6e4-416effcea6a5' class='xr-var-data-in' type='checkbox'><label for='data-1c0154db-57a0-46c3-b6e4-416effcea6a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab43525a-2b36-4e68-b78d-17f937f95984' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab43525a-2b36-4e68-b78d-17f937f95984' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std</span></div><div class='xr-var-dims'>(log_gdp_std_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8797 0.9648 ... 0.781 0.9186</div><input id='attrs-cd1cad43-93d1-42f4-b48d-94a453e77593' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd1cad43-93d1-42f4-b48d-94a453e77593' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd47b223-bcf0-430e-af6f-c23c60a8a104' class='xr-var-data-in' type='checkbox'><label for='data-fd47b223-bcf0-430e-af6f-c23c60a8a104' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.87971187, 0.9647547 , 1.1662705 , 1.10448536, 0.91490375,\n",
       "       1.0816501 , 1.19091827, 1.20635083, 0.92191149, 0.75522974,\n",
       "       1.19932025, 0.80615567, 0.81085767, 0.85700117, 1.02100708,\n",
       "       1.13604626, 1.14353554, 1.00673891, 0.99526755, 1.01882817,\n",
       "       0.91377709, 1.04445526, 1.13125035, 1.05074296, 0.82725418,\n",
       "       1.1992612 , 1.21101002, 1.07051814, 0.97168624, 0.86442499,\n",
       "       0.88532949, 0.76378895, 0.80593281, 1.02608907, 0.87809731,\n",
       "       0.99665294, 1.06397383, 1.16462671, 1.13188329, 1.19121349,\n",
       "       0.88528458, 1.02007148, 1.20538601, 1.0291909 , 1.0094337 ,\n",
       "       0.95380637, 0.96138451, 0.80021355, 1.17270317, 1.0785727 ,\n",
       "       0.7578708 , 1.19154684, 0.99882645, 1.19220721, 1.02371474,\n",
       "       1.19506889, 0.8851899 , 0.88595898, 0.89099694, 0.87527671,\n",
       "       0.78224406, 1.13047194, 1.14636797, 1.04818317, 0.97305583,\n",
       "       0.97590885, 1.19265328, 0.91891832, 1.07593003, 0.87995217,\n",
       "       1.11419863, 0.94113391, 0.91456917, 1.2124455 , 1.01797366,\n",
       "       1.20611401, 1.18348401, 1.1889489 , 0.96307105, 0.96922103,\n",
       "       1.19456727, 0.99007688, 0.81322267, 0.86325463, 0.8838469 ,\n",
       "       1.09495636, 1.13783355, 1.1362733 , 0.86399746, 0.97999287,\n",
       "       1.01374697, 0.96226885, 0.89940883, 1.06594471, 1.28736127,\n",
       "       1.05377081, 1.15413994, 0.95859462, 0.84465573, 0.78849201,\n",
       "       1.06962336, 1.0225778 , 0.78181112, 1.15206046, 0.86771734,\n",
       "       0.79561618, 0.8753453 , 1.0768738 , 0.74834587, 1.06805786,\n",
       "       1.0225421 , 1.17470978, 0.76969203, 0.79637985, 0.95042471,\n",
       "       1.20481088, 1.22579142, 0.84389598, 1.16049201, 1.10972919,\n",
       "       0.8879629 , 1.02458815, 0.99330541, 0.9746153 , 0.91014275,\n",
       "       1.08601201, 1.17525067, 1.15205757, 0.98901498, 1.18678542,\n",
       "       1.0192294 , 1.04110842, 0.81555521, 1.10640269, 0.87214242,\n",
       "       0.85345509, 1.18226546, 0.88406187, 0.72155551, 0.99007276,\n",
       "       1.09578033, 1.14281499, 1.19312592, 0.99227962, 1.15012638,\n",
       "       0.9491481 , 0.78790809, 0.85360006, 1.02676137, 0.78269215,\n",
       "       0.96362185, 1.0320481 , 1.0686262 , 1.02623744, 1.03100012,\n",
       "       0.73467768, 0.83663241, 0.97695723, 1.06613588, 1.2249729 ,\n",
       "       0.85987283, 1.00761228, 1.01508609, 0.89311399, 0.94658336,\n",
       "       0.99668099, 0.78303247, 1.0743655 , 0.78096666, 0.91858918])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e161ed8-b2fb-4cb7-999a-f15d6f7f4e7b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e161ed8-b2fb-4cb7-999a-f15d6f7f4e7b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bf85285b-aa3d-4e23-ac37-54c1663ffbfe' class='xr-index-data-in' type='checkbox'/><label for='index-bf85285b-aa3d-4e23-ac37-54c1663ffbfe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-342f7776-3c1d-4449-8368-b9e485531b1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-342f7776-3c1d-4449-8368-b9e485531b1e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:27.082048</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
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       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> sample_stats_prior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stan_data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bf021052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.19925646, -0.13838364])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha_a1 = stan_data3.posterior.alpha.sel(africa=0)\n",
    "alpha_a2 = stan_data3.posterior.alpha.sel(africa=1)\n",
    "\n",
    "diff_alpha_a1_a2 = az.extract(alpha_a1 - alpha_a2).alpha.values\n",
    "\n",
    "az.hdi(diff_alpha_a1_a2, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05df4e93",
   "metadata": {},
   "source": [
    "### R Code 8.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c5eb4104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract 200 samples from arviz-fit to numpy\n",
    "params_post = az.extract(stan_data3.posterior, num_samples=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "33c0367d",
   "metadata": {},
   "outputs": [],
   "source": [
    "rugged_seq = np.linspace(0, 1, 30)\n",
    "\n",
    "log_gdp_mean_africa = []\n",
    "log_gdp_hdi_africa = []\n",
    "\n",
    "log_gdp_mean_not_africa = []\n",
    "log_gdp_hdi_not_africa = []\n",
    "\n",
    "# Calculation posterior mean and interval HDI\n",
    "for i in range(len(rugged_seq)):\n",
    "        log_gdp_africa = params_post.alpha.sel(africa=0) + params_post.beta.values * rugged_seq[i]\n",
    "        log_gdp_mean_africa.append(np.mean(log_gdp_africa.values))\n",
    "        log_gdp_hdi_africa.append(az.hdi(log_gdp_africa.values, hdi_prob=0.89))\n",
    "        \n",
    "        log_gdp_not_africa = params_post.alpha.sel(africa=1) + params_post.beta.values * rugged_seq[i]\n",
    "        log_gdp_mean_not_africa.append(np.mean(log_gdp_not_africa.values))\n",
    "        log_gdp_hdi_not_africa.append(az.hdi(log_gdp_not_africa.values, hdi_prob=0.89))\n",
    "        \n",
    "log_gdp_hdi_africa = np.array(log_gdp_hdi_africa)\n",
    "log_gdp_hdi_not_africa = np.array(log_gdp_hdi_not_africa) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58668e37",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 8))\n",
    "\n",
    "plt.plot(rugged_seq, log_gdp_mean_africa, c='blue')\n",
    "plt.fill_between(rugged_seq, log_gdp_hdi_africa[:,0], log_gdp_hdi_africa[:,1], color='blue', alpha=0.3)\n",
    "\n",
    "plt.plot(rugged_seq, log_gdp_mean_not_africa, c='black')\n",
    "plt.fill_between(rugged_seq, log_gdp_hdi_not_africa[:,0], log_gdp_hdi_not_africa[:,1], color='darkgray', alpha=0.3)\n",
    "\n",
    "plt.plot(ddf.loc[ddf.cid == 1,'rugged_std'], ddf.loc[ddf.cid==1, 'log_gdp_std'], 'o', markerfacecolor='blue', color='gray')\n",
    "plt.plot(ddf.loc[ddf.cid == 2,'rugged_std'], ddf.loc[ddf.cid==2, 'log_gdp_std'], 'o', markerfacecolor='none', color='black')\n",
    "\n",
    "plt.title('m8.4')\n",
    "plt.xlabel('ruggedness (standardized)')\n",
    "plt.ylabel('log GDP (as proportion of mean)')\n",
    "\n",
    "plt.text(0.8, 1.04, 'Not Africa', fontsize='x-large', color='black')\n",
    "plt.text(0.8, 0.86, 'Africa',  fontsize='x-large', color='darkblue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6372212",
   "metadata": {},
   "source": [
    "### R Code 8.13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7e41b998",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mMessages from \u001b[0m\u001b[36;1mstanc\u001b[0m\u001b[36m:\u001b[0m\n",
      "Warning in '/tmp/httpstan__g9c_c3n/model_5jmion4o.stan', line 11, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/tmp/httpstan__g9c_c3n/model_5jmion4o.stan', line 12, column 8: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.33.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_wn5k80fz/model_5jmion4o.stan', line 32, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 3.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_wn5k80fz/model_5jmion4o.stan', line 32, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 2.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_wn5k80fz/model_5jmion4o.stan', line 32, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 2.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_wn5k80fz/model_5jmion4o.stan', line 32, column 8 to column 40)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n"
     ]
    }
   ],
   "source": [
    "model4 = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] log_gdp_std;\n",
    "        vector[N] rugged_std;\n",
    "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
    "        real rugged_std_average;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
    "        real beta[2];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            mu[i] = alpha[ cid[i] ] + beta[ cid[i] ] * (rugged_std[i] - rugged_std_average);\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        \n",
    "        alpha ~ normal(1, 0.1);\n",
    "        beta ~ normal(0, 0.3);\n",
    "        sigma ~ exponential(1);\n",
    "        \n",
    "        // Likelihood\n",
    "        log_gdp_std ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;\n",
    "        vector[N] log_gdp_std_hat;\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
    "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(ddf),\n",
    "    'log_gdp_std': ddf['log_gdp_std'].values,\n",
    "    'rugged_std': ddf['rugged_std'].values,\n",
    "    'rugged_std_average': ddf['rugged_std'].mean(),\n",
    "    'cid': ddf['cid'].values,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model4, data=data)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7d9bc956",
   "metadata": {},
   "outputs": [],
   "source": [
    "stan_data4 = az.from_pystan(\n",
    "    posterior=samples,\n",
    "    posterior_predictive=\"log_gdp_std_hat\",\n",
    "    observed_data=['log_gdp_std'],\n",
    "    prior=samples,\n",
    "    prior_model=posteriori,\n",
    "    posterior_model=posteriori,\n",
    "    dims={\n",
    "        \"alpha\": [\"africa\"],\n",
    "        \"beta\": [\"africa\"],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fafdac5",
   "metadata": {},
   "source": [
    "### R Code 8.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b88d50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[0]</th>\n",
       "      <td>0.887</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5156.0</td>\n",
       "      <td>2914.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>1.051</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.033</td>\n",
       "      <td>1.071</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5063.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[0]</th>\n",
       "      <td>0.131</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4710.0</td>\n",
       "      <td>3386.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.247</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4727.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.112</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>3262.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha[0]  0.887  0.016   0.858    0.916      0.000    0.000    5156.0   \n",
       "alpha[1]  1.051  0.010   1.033    1.071      0.000    0.000    5063.0   \n",
       "beta[0]   0.131  0.075  -0.006    0.270      0.001    0.001    4710.0   \n",
       "beta[1]  -0.143  0.056  -0.247   -0.036      0.001    0.001    4727.0   \n",
       "sigma     0.112  0.006   0.100    0.123      0.000    0.000    5069.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "alpha[0]    2914.0    1.0  \n",
       "alpha[1]    3095.0    1.0  \n",
       "beta[0]     3386.0    1.0  \n",
       "beta[1]     3117.0    1.0  \n",
       "sigma       3262.0    1.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(stan_data4, var_names=['alpha', 'beta', 'sigma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8df41056",
   "metadata": {},
   "source": [
    "### R Code 8.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91881f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>elpd_loo</th>\n",
       "      <th>p_loo</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m8.3</th>\n",
       "      <td>0</td>\n",
       "      <td>129.589277</td>\n",
       "      <td>4.989499</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.870793</td>\n",
       "      <td>7.309693</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m8.2</th>\n",
       "      <td>1</td>\n",
       "      <td>126.137495</td>\n",
       "      <td>4.145890</td>\n",
       "      <td>3.451783</td>\n",
       "      <td>0.129207</td>\n",
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       "      <td>3.225278</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
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       "    <tr>\n",
       "      <th>m8.1</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>7.448111</td>\n",
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      "text/plain": [
       "      rank    elpd_loo     p_loo  elpd_diff    weight        se       dse  \\\n",
       "m8.3     0  129.589277  4.989499   0.000000  0.870793  7.309693  0.000000   \n",
       "m8.2     1  126.137495  4.145890   3.451783  0.129207  7.405339  3.225278   \n",
       "m8.1     2   94.492463  2.498616  35.096814  0.000000  6.464447  7.448111   \n",
       "\n",
       "      warning scale  \n",
       "m8.3    False   log  \n",
       "m8.2    False   log  \n",
       "m8.1    False   log  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_8 = { 'm8.1': stan_data2, 'm8.2': stan_data3, 'm8.3': stan_data4 }\n",
    "\n",
    "# https://python.arviz.org/en/stable/api/generated/arviz.compare.html\n",
    "# https://rss.onlinelibrary.wiley.com/doi/10.1111/1467-9868.00353\n",
    "az.compare(models_8, ic='loo')  # loo is same that PSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a0cf9c",
   "metadata": {},
   "source": [
    "### R Code 8.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "176c1ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Computed from 4000 posterior samples and 170 observations log-likelihood matrix.\n",
       "\n",
       "         Estimate       SE\n",
       "elpd_loo   129.59     7.31\n",
       "p_loo        4.99        -\n",
       "------\n",
       "\n",
       "Pareto k diagnostic values:\n",
       "                         Count   Pct.\n",
       "(-Inf, 0.5]   (good)      169   99.4%\n",
       " (0.5, 0.7]   (ok)          1    0.6%\n",
       "   (0.7, 1]   (bad)         0    0.0%\n",
       "   (1, Inf)   (very bad)    0    0.0%"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.loo(stan_data4, pointwise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b231c777",
   "metadata": {},
   "outputs": [
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       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (chain: 4, draw: 1000, africa: 2, mu_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 0 1 2 3\n",
       "  * draw      (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * africa    (africa) int64 0 1\n",
       "  * mu_dim_0  (mu_dim_0) int64 0 1 2 3 4 5 6 7 ... 163 164 165 166 167 168 169\n",
       "Data variables:\n",
       "    alpha     (chain, draw, africa) float64 0.88 1.051 0.8771 ... 0.876 1.022\n",
       "    beta      (chain, draw, africa) float64 0.2193 -0.1387 ... 0.1464 -0.1622\n",
       "    sigma     (chain, draw) float64 0.1212 0.131 0.1102 ... 0.1118 0.1209 0.1094\n",
       "    mu        (chain, draw, mu_dim_0) float64 0.8632 1.004 ... 0.8572 0.8728\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.413832\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/5jmion4o\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1c2d85bc-b36c-407b-aeb6-23ad7168e168' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1c2d85bc-b36c-407b-aeb6-23ad7168e168' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>africa</span>: 2</li><li><span class='xr-has-index'>mu_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-91819c40-1408-4df9-be45-db0cc9f74d5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91819c40-1408-4df9-be45-db0cc9f74d5f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-950d7c1c-d44a-4556-a156-7aa3cf2b0d3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-950d7c1c-d44a-4556-a156-7aa3cf2b0d3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f581d0-23ae-487c-86cf-6425fe9c1040' class='xr-var-data-in' type='checkbox'><label for='data-62f581d0-23ae-487c-86cf-6425fe9c1040' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-3d6d2856-8996-4442-99e8-b87c6570c80c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d6d2856-8996-4442-99e8-b87c6570c80c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65922343-4bbd-4b79-9fa4-ebd8d0bf31a7' class='xr-var-data-in' type='checkbox'><label for='data-65922343-4bbd-4b79-9fa4-ebd8d0bf31a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>africa</span></div><div class='xr-var-dims'>(africa)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-ff4a61db-26f2-47ff-b619-0ff973a644d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff4a61db-26f2-47ff-b619-0ff973a644d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2117f09-47a7-4711-bfcf-42a71cf6029a' class='xr-var-data-in' type='checkbox'><label for='data-a2117f09-47a7-4711-bfcf-42a71cf6029a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-0e6a6210-5136-4220-889d-2b84e3ee7dac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e6a6210-5136-4220-889d-2b84e3ee7dac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a05256b-a612-4bca-a870-91bea9fbfe8d' class='xr-var-data-in' type='checkbox'><label for='data-6a05256b-a612-4bca-a870-91bea9fbfe8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-117d0bba-0410-47ee-9175-2bd1bc398e2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-117d0bba-0410-47ee-9175-2bd1bc398e2f' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw, africa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.88 1.051 0.8771 ... 0.876 1.022</div><input id='attrs-8505f7b8-d76b-43f7-9b95-fdfbd41600ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8505f7b8-d76b-43f7-9b95-fdfbd41600ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d4704ca-3d26-4664-aff9-3151381e4a47' class='xr-var-data-in' type='checkbox'><label for='data-9d4704ca-3d26-4664-aff9-3151381e4a47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.8799981 , 1.05065779],\n",
       "        [0.87713665, 1.04325154],\n",
       "        [0.87348815, 1.04485125],\n",
       "        ...,\n",
       "        [0.87525775, 1.0649068 ],\n",
       "        [0.92603814, 1.03278517],\n",
       "        [0.87511528, 1.05470343]],\n",
       "\n",
       "       [[0.85667038, 1.06896312],\n",
       "        [0.8684841 , 1.05543232],\n",
       "        [0.88009216, 1.05577594],\n",
       "        ...,\n",
       "        [0.88103403, 1.05999608],\n",
       "        [0.88668431, 1.05804938],\n",
       "        [0.8934676 , 1.04821274]],\n",
       "\n",
       "       [[0.86320487, 1.0615367 ],\n",
       "        [0.88711142, 1.0437661 ],\n",
       "        [0.87645157, 1.05434753],\n",
       "        ...,\n",
       "        [0.91249938, 1.02800977],\n",
       "        [0.86577335, 1.07185186],\n",
       "        [0.87611937, 1.05781191]],\n",
       "\n",
       "       [[0.86701879, 1.06110604],\n",
       "        [0.87139506, 1.04891813],\n",
       "        [0.90980155, 1.04566211],\n",
       "        ...,\n",
       "        [0.89693459, 1.04238248],\n",
       "        [0.89880551, 1.05136368],\n",
       "        [0.87604605, 1.02221448]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw, africa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2193 -0.1387 ... 0.1464 -0.1622</div><input id='attrs-b4629c26-fc63-4846-a078-9a71a1a75567' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4629c26-fc63-4846-a078-9a71a1a75567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff5b3f43-c5c7-422a-82f9-7334d2152001' class='xr-var-data-in' type='checkbox'><label for='data-ff5b3f43-c5c7-422a-82f9-7334d2152001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.21926906, -0.13874595],\n",
       "        [ 0.16738814, -0.0141583 ],\n",
       "        [ 0.33900366, -0.09472518],\n",
       "        ...,\n",
       "        [ 0.21318697, -0.12580214],\n",
       "        [ 0.14861179, -0.07904649],\n",
       "        [ 0.08399365, -0.02291149]],\n",
       "\n",
       "       [[ 0.14185605, -0.08482378],\n",
       "        [-0.0547438 , -0.17420287],\n",
       "        [ 0.19536518, -0.17603927],\n",
       "        ...,\n",
       "        [ 0.03362215, -0.1933    ],\n",
       "        [ 0.04585387, -0.19789593],\n",
       "        [ 0.10939905, -0.14815578]],\n",
       "\n",
       "       [[ 0.14183397, -0.04965893],\n",
       "        [ 0.23157524, -0.22574906],\n",
       "        [ 0.15343804, -0.0636724 ],\n",
       "        ...,\n",
       "        [ 0.08964895, -0.16182891],\n",
       "        [ 0.17608494, -0.13570401],\n",
       "        [ 0.12740187, -0.12255009]],\n",
       "\n",
       "       [[ 0.11241455, -0.10339285],\n",
       "        [ 0.11061567, -0.05462318],\n",
       "        [ 0.22438704, -0.14614973],\n",
       "        ...,\n",
       "        [ 0.07025921, -0.13957218],\n",
       "        [ 0.13794591, -0.09530278],\n",
       "        [ 0.1464107 , -0.16219026]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1212 0.131 ... 0.1209 0.1094</div><input id='attrs-4ec94dea-3eff-4c6f-8b7e-822618562373' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ec94dea-3eff-4c6f-8b7e-822618562373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df9e57f-250b-4e1d-982c-63d990a68a84' class='xr-var-data-in' type='checkbox'><label for='data-8df9e57f-250b-4e1d-982c-63d990a68a84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.12120657, 0.13103159, 0.11016947, ..., 0.11075485, 0.12176888,\n",
       "        0.11407225],\n",
       "       [0.10600995, 0.11391783, 0.11305613, ..., 0.10055508, 0.10057636,\n",
       "        0.10525774],\n",
       "       [0.10893276, 0.10850748, 0.11185106, ..., 0.11659326, 0.10601317,\n",
       "        0.11926662],\n",
       "       [0.09931887, 0.10542284, 0.10944904, ..., 0.11180329, 0.12094873,\n",
       "        0.10941977]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8632 1.004 ... 0.8572 0.8728</div><input id='attrs-fee0ee56-77a7-455b-87db-239698527c15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fee0ee56-77a7-455b-87db-239698527c15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff97be28-b67d-4576-ae2f-240beb8f9af0' class='xr-var-data-in' type='checkbox'><label for='data-ff97be28-b67d-4576-ae2f-240beb8f9af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.86319823, 1.00381665, 1.0632792 , ..., 0.89512341,\n",
       "         0.85170799, 0.87507736],\n",
       "        [0.86431178, 1.03847165, 1.04453949, ..., 0.88868319,\n",
       "         0.85554023, 0.87338021],\n",
       "        [0.84751451, 1.01287168, 1.05346819, ..., 0.89687283,\n",
       "         0.82974988, 0.86588039],\n",
       "        ...,\n",
       "        [0.85892387, 1.02243554, 1.07635074, ..., 0.88996351,\n",
       "         0.84775235, 0.87047351],\n",
       "        [0.91465186, 1.00609879, 1.03997586, ..., 0.93628947,\n",
       "         0.90686424, 0.92270306],\n",
       "        [0.86867988, 1.04696843, 1.05678764, ..., 0.88090921,\n",
       "         0.86427841, 0.87323033]],\n",
       "\n",
       "       [[0.84580171, 1.04032631, 1.07667936, ..., 0.8664557 ,\n",
       "         0.83836811, 0.85348691],\n",
       "        [0.87267844, 0.9966208 , 1.07127918, ..., 0.86470784,\n",
       "         0.87554715, 0.86971264],\n",
       "        [0.86512375, 0.99634444, 1.07178984, ..., 0.89356857,\n",
       "         0.85488614, 0.87570787],\n",
       "...\n",
       "        [0.9056307 , 0.97337575, 1.04273099, ..., 0.91868342,\n",
       "         0.90093287, 0.91048752],\n",
       "        [0.85228214, 1.0260377 , 1.08419656, ..., 0.87791979,\n",
       "         0.84305486, 0.86182173],\n",
       "        [0.86635814, 1.01643856, 1.06896003, ..., 0.88490762,\n",
       "         0.85968197, 0.87326028]],\n",
       "\n",
       "       [[0.85840586, 1.02620025, 1.07051147, ..., 0.87477322,\n",
       "         0.85251506, 0.86449604],\n",
       "        [0.86291996, 1.03047715, 1.05388709, ..., 0.8790254 ,\n",
       "         0.85712342, 0.86891268],\n",
       "        [0.89260956, 0.99632143, 1.05895703, ..., 0.9252799 ,\n",
       "         0.88085113, 0.90476597],\n",
       "        ...,\n",
       "        [0.89155149, 0.99526241, 1.05507906, ..., 0.90178111,\n",
       "         0.88786974, 0.89535786],\n",
       "        [0.88823643, 1.01918912, 1.06003317, ..., 0.9083211 ,\n",
       "         0.88100772, 0.89570979],\n",
       "        [0.86482842, 0.96745846, 1.03696857, ..., 0.88614555,\n",
       "         0.85715614, 0.87276037]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c2c072e-8a84-429e-a8bd-cafae27c97b2' class='xr-section-summary-in' type='checkbox'  ><label for='section-3c2c072e-8a84-429e-a8bd-cafae27c97b2' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9a9fcd2d-c9bc-421e-8127-f69ee9810734' class='xr-index-data-in' type='checkbox'/><label for='index-9a9fcd2d-c9bc-421e-8127-f69ee9810734' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fbd50487-6242-4e73-8723-4f470b2cb40f' class='xr-index-data-in' type='checkbox'/><label for='index-fbd50487-6242-4e73-8723-4f470b2cb40f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>africa</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db64981e-a0ad-43d4-b93d-1956dc5c4198' class='xr-index-data-in' type='checkbox'/><label for='index-db64981e-a0ad-43d4-b93d-1956dc5c4198' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;africa&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84e2f0ad-b3c1-4b85-b16c-dd5e3ffca557' class='xr-index-data-in' type='checkbox'/><label for='index-84e2f0ad-b3c1-4b85-b16c-dd5e3ffca557' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48c8f476-f120-4a48-953a-55e09852e1f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-48c8f476-f120-4a48-953a-55e09852e1f4' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.413832</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/5jmion4o</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta[2];\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta[ cid[i] ] * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior_predictivee76ea032-eb72-48ab-9f82-5d05baff79c7\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
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       "  border-bottom: solid 1px var(--xr-border-color);\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       ".xr-array-name {\n",
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       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000, log_gdp_std_hat_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "  * log_gdp_std_hat_dim_0  (log_gdp_std_hat_dim_0) int64 0 1 2 3 ... 167 168 169\n",
       "Data variables:\n",
       "    log_gdp_std_hat        (chain, draw, log_gdp_std_hat_dim_0) float64 0.913...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.571354\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bc5bff55-9123-4709-bdad-bee6dc532c7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc5bff55-9123-4709-bdad-bee6dc532c7c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_gdp_std_hat_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ba45f7c-8a12-4440-9a9e-81400ac6215a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ba45f7c-8a12-4440-9a9e-81400ac6215a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-f1b1c424-64cb-4a9f-a2d6-6e1ff49e9fdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1b1c424-64cb-4a9f-a2d6-6e1ff49e9fdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ed68687-6e59-4a95-9ecf-731dc699e811' class='xr-var-data-in' type='checkbox'><label for='data-9ed68687-6e59-4a95-9ecf-731dc699e811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-54fdfe38-259a-4f43-872d-cd1d022580e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54fdfe38-259a-4f43-872d-cd1d022580e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9826fede-7f5c-453f-a44b-1a1eb7e8175c' class='xr-var-data-in' type='checkbox'><label for='data-9826fede-7f5c-453f-a44b-1a1eb7e8175c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_hat_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-4bfcdf28-ef84-4f64-8a50-fbbc9cd7dce1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bfcdf28-ef84-4f64-8a50-fbbc9cd7dce1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53f4645d-8377-4f14-90d2-fc78c42e4e7f' class='xr-var-data-in' type='checkbox'><label for='data-53f4645d-8377-4f14-90d2-fc78c42e4e7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e5fcdbb-2d92-401b-ae06-43deca13f83e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e5fcdbb-2d92-401b-ae06-43deca13f83e' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std_hat</span></div><div class='xr-var-dims'>(chain, draw, log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9133 1.06 0.9788 ... 0.922 0.7556</div><input id='attrs-3277e424-7b71-4fd9-9662-a17d4d33b24b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3277e424-7b71-4fd9-9662-a17d4d33b24b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e5ded7-899b-4100-8ab8-769400c9bf5e' class='xr-var-data-in' type='checkbox'><label for='data-68e5ded7-899b-4100-8ab8-769400c9bf5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.91334535, 1.05970046, 0.97881659, ..., 0.83040664,\n",
       "         0.70359595, 1.05637005],\n",
       "        [0.71546209, 1.10500894, 1.30937776, ..., 0.92730361,\n",
       "         0.97688805, 0.97723416],\n",
       "        [0.95760535, 1.05530868, 1.1253216 , ..., 1.07533115,\n",
       "         0.81456514, 0.99479222],\n",
       "        ...,\n",
       "        [0.97217478, 1.09339357, 1.14342635, ..., 0.78115254,\n",
       "         0.95865994, 0.87245012],\n",
       "        [0.87386487, 1.11104821, 1.07002512, ..., 1.07274116,\n",
       "         1.02435039, 0.80489117],\n",
       "        [0.94640017, 0.904975  , 1.03796144, ..., 0.84437191,\n",
       "         0.79725304, 0.84477131]],\n",
       "\n",
       "       [[0.90745317, 1.03873503, 1.21806687, ..., 0.84733837,\n",
       "         0.69738243, 0.6929596 ],\n",
       "        [0.88046253, 0.88200227, 1.06160769, ..., 0.82347294,\n",
       "         0.99758956, 0.8216173 ],\n",
       "        [0.75864997, 1.08206765, 1.10102419, ..., 0.81515156,\n",
       "         0.72599967, 0.99645503],\n",
       "...\n",
       "        [0.7921488 , 0.98673542, 1.24539948, ..., 0.86309635,\n",
       "         0.69700953, 0.57322407],\n",
       "        [0.83691493, 0.86983215, 1.02955418, ..., 0.75501554,\n",
       "         0.99012119, 1.01352251],\n",
       "        [0.87615987, 1.1254726 , 1.03469804, ..., 0.86705399,\n",
       "         0.94522565, 0.82651532]],\n",
       "\n",
       "       [[0.81030272, 1.00444259, 1.00256717, ..., 1.05497432,\n",
       "         0.91606441, 1.06542145],\n",
       "        [0.58475272, 1.13150744, 1.17129389, ..., 0.934131  ,\n",
       "         0.93786   , 0.90262724],\n",
       "        [0.85820583, 1.03871165, 0.91714019, ..., 0.80313385,\n",
       "         0.85317569, 1.06026125],\n",
       "        ...,\n",
       "        [0.69520517, 0.98274854, 0.98247656, ..., 1.2069157 ,\n",
       "         0.79129649, 1.05382379],\n",
       "        [1.15529022, 0.980254  , 0.98750211, ..., 0.79397704,\n",
       "         0.82315815, 0.98375107],\n",
       "        [1.03550361, 0.82682667, 1.12376824, ..., 0.9299621 ,\n",
       "         0.92202406, 0.75560522]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a6e7260-8ec0-4306-991b-ecd640bb34ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-4a6e7260-8ec0-4306-991b-ecd640bb34ac' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cea85d4a-a117-4759-94c0-c2d9dcbbf13a' class='xr-index-data-in' type='checkbox'/><label for='index-cea85d4a-a117-4759-94c0-c2d9dcbbf13a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-307c0194-41dd-479d-8c1d-162f74e60b54' class='xr-index-data-in' type='checkbox'/><label for='index-307c0194-41dd-479d-8c1d-162f74e60b54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d4b55f1-4b4c-4abb-8374-e4377665386a' class='xr-index-data-in' type='checkbox'/><label for='index-3d4b55f1-4b4c-4abb-8374-e4377665386a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_hat_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-023801c4-2ad1-48fd-a549-0c0f19e70b23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-023801c4-2ad1-48fd-a549-0c0f19e70b23' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.571354</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihood1dc42d7a-a29a-42f9-8e37-471a3df75be0\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (chain: 4, draw: 1000, log_lik_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain          (chain) int64 0 1 2 3\n",
       "  * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_lik_dim_0  (log_lik_dim_0) int64 0 1 2 3 4 5 ... 164 165 166 167 168 169\n",
       "Data variables:\n",
       "    log_lik        (chain, draw, log_lik_dim_0) float64 1.182 1.139 ... 1.206\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.520437\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f9170613-abda-4bd5-86ae-406f87908985' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f9170613-abda-4bd5-86ae-406f87908985' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-88a8a6f8-0cda-4890-838f-36055ab1e89f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88a8a6f8-0cda-4890-838f-36055ab1e89f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-b3e709d6-1fef-4240-91da-afeb94b9486c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3e709d6-1fef-4240-91da-afeb94b9486c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c0b7e2-44ac-436b-b42d-37ff86253a8d' class='xr-var-data-in' type='checkbox'><label for='data-91c0b7e2-44ac-436b-b42d-37ff86253a8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-cedc25a8-25d3-4476-9119-00e20dd3c7c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cedc25a8-25d3-4476-9119-00e20dd3c7c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e8409f-5985-4a47-bd55-6edd5d21f0b7' class='xr-var-data-in' type='checkbox'><label for='data-51e8409f-5985-4a47-bd55-6edd5d21f0b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-c4bd7857-7228-44a4-aeaa-46f4714370ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4bd7857-7228-44a4-aeaa-46f4714370ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-995e20da-5b45-4bf7-81cd-16b4f67f2f83' class='xr-var-data-in' type='checkbox'><label for='data-995e20da-5b45-4bf7-81cd-16b4f67f2f83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea0d075f-0c6b-4ffd-93de-674d8fea90fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea0d075f-0c6b-4ffd-93de-674d8fea90fc' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.182 1.139 0.8303 ... 1.051 1.206</div><input id='attrs-962e7097-ebde-49f1-b482-1cc5bc890657' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-962e7097-ebde-49f1-b482-1cc5bc890657' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c3aa562-4d13-4e06-be20-934a86e46c70' class='xr-var-data-in' type='checkbox'><label for='data-4c3aa562-4d13-4e06-be20-934a86e46c70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.1820393 ,  1.13938961,  0.8303109 , ...,  0.09787357,\n",
       "          1.02100108,  1.12688395],\n",
       "        [ 1.10647169,  0.955125  ,  0.68183897, ...,  0.10931974,\n",
       "          0.95142574,  1.0538577 ],\n",
       "        [ 1.24409104,  1.1914198 ,  0.76261332, ..., -0.01100477,\n",
       "          1.1887603 ,  1.17234755],\n",
       "        ...,\n",
       "        [ 1.26388307,  1.14588255,  0.9519222 , ..., -0.10454219,\n",
       "          1.09969021,  1.18713108],\n",
       "        [ 1.14552552,  1.12905189,  0.64883444, ...,  0.54380592,\n",
       "          0.65221112,  1.18612124],\n",
       "        [ 1.24730823,  0.99226869,  0.79140774, ..., -0.18607086,\n",
       "          0.98528502,  1.17292883]],\n",
       "\n",
       "       [[ 1.27412312,  1.07118996,  0.96816979, ..., -0.59792608,\n",
       "          1.1786877 ,  1.13671538],\n",
       "        [ 1.25143334,  1.21421521,  0.90567957, ..., -0.44024966,\n",
       "          0.90868025,  1.16129702],\n",
       "        [ 1.25260737,  1.22189551,  0.9117377 , ..., -0.01775354,\n",
       "          1.04718556,  1.189001  ],\n",
       "...\n",
       "        [ 1.20541632,  1.22739159,  0.66877386, ...,  0.33866842,\n",
       "          0.70077761,  1.22771106],\n",
       "        [ 1.29178055,  1.15817115,  1.02557127, ..., -0.39160888,\n",
       "          1.15375171,  1.18188647],\n",
       "        [ 1.20118711,  1.11356031,  0.87460313, ..., -0.05424875,\n",
       "          0.98965888,  1.13523105]],\n",
       "\n",
       "       [[ 1.36747152,  1.19910525,  0.92568134, ..., -0.62878685,\n",
       "          1.1309997 ,  1.24216421],\n",
       "        [ 1.31815211,  1.13651266,  0.7626325 , ..., -0.38581898,\n",
       "          1.06991132,  1.21981706],\n",
       "        [ 1.28641429,  1.25176609,  0.81267934, ...,  0.3656366 ,\n",
       "          0.8769277 ,  1.28538204],\n",
       "        ...,\n",
       "        [ 1.26646864,  1.23484683,  0.77753335, ...,  0.08065864,\n",
       "          0.81494413,  1.25048791],\n",
       "        [ 1.19096623,  1.09217212,  0.80768604, ...,  0.25109225,\n",
       "          0.85137293,  1.17555811],\n",
       "        [ 1.28437418,  1.29331983,  0.5954114 , ..., -0.18585682,\n",
       "          1.05120516,  1.20591383]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2dff35ac-a325-4272-b4f4-9f1c7b437712' class='xr-section-summary-in' type='checkbox'  ><label for='section-2dff35ac-a325-4272-b4f4-9f1c7b437712' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-816463c2-627a-41ff-bc3f-b588b5c7ea27' class='xr-index-data-in' type='checkbox'/><label for='index-816463c2-627a-41ff-bc3f-b588b5c7ea27' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81c701b5-aa54-459f-8308-ce9d23283225' class='xr-index-data-in' type='checkbox'/><label for='index-81c701b5-aa54-459f-8308-ce9d23283225' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0d92b6a7-5236-4e3d-a530-b7dcdec1efc5' class='xr-index-data-in' type='checkbox'/><label for='index-0d92b6a7-5236-4e3d-a530-b7dcdec1efc5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-06e34b79-8b06-4b02-8b2d-88b4033eca7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06e34b79-8b06-4b02-8b2d-88b4033eca7c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.520437</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats8928e675-5495-4f2a-94fb-17c822a95713\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       ".xr-header {\n",
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       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
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       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "\n",
       ".xr-var-item > div,\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    acceptance_rate  (chain, draw) float64 0.9562 0.7774 ... 0.9232 0.8468\n",
       "    step_size        (chain, draw) float64 0.7482 0.7482 ... 0.6294 0.6294\n",
       "    tree_depth       (chain, draw) int64 2 3 3 3 2 3 3 3 2 ... 3 3 2 3 3 3 3 2 3\n",
       "    n_steps          (chain, draw) int64 3 7 7 7 3 7 7 7 3 ... 7 7 7 7 7 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -280.2 -279.4 ... -284.5 -279.4\n",
       "    lp               (chain, draw) float64 285.4 280.7 282.5 ... 285.7 283.1\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.463167\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/5jmion4o\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6d8d029a-6277-4302-9723-c6bf59298fc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d8d029a-6277-4302-9723-c6bf59298fc5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f15b3be3-3a67-42bb-a9a4-8644d96cdeea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f15b3be3-3a67-42bb-a9a4-8644d96cdeea' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-c528bfb8-6cb8-442e-bd8b-b75aee721bbb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c528bfb8-6cb8-442e-bd8b-b75aee721bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba947733-9850-4da1-91be-eba99029a73e' class='xr-var-data-in' type='checkbox'><label for='data-ba947733-9850-4da1-91be-eba99029a73e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-721bc922-c541-4e18-ab25-d69fa64b82dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-721bc922-c541-4e18-ab25-d69fa64b82dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a86588de-a9a8-4b1b-b798-260084c13912' class='xr-var-data-in' type='checkbox'><label for='data-a86588de-a9a8-4b1b-b798-260084c13912' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e372c0db-8ffc-4f16-83d8-5b898b138021' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e372c0db-8ffc-4f16-83d8-5b898b138021' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9562 0.7774 ... 0.9232 0.8468</div><input id='attrs-60b42f97-d8fd-468a-8828-9c8fc3499ed6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60b42f97-d8fd-468a-8828-9c8fc3499ed6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-770bfed6-6bb6-44ca-8f68-9b94a9930305' class='xr-var-data-in' type='checkbox'><label for='data-770bfed6-6bb6-44ca-8f68-9b94a9930305' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.9561964 , 0.77743986, 0.97084456, ..., 0.85410249, 0.74106279,\n",
       "        1.        ],\n",
       "       [0.87203886, 0.97887694, 0.96976632, ..., 0.69208485, 1.        ,\n",
       "        0.91256317],\n",
       "       [0.76677045, 0.81393291, 0.96968841, ..., 0.6303582 , 0.99270845,\n",
       "        1.        ],\n",
       "       [0.98556169, 0.95836686, 0.99178639, ..., 0.90313257, 0.92318978,\n",
       "        0.84679371]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7482 0.7482 ... 0.6294 0.6294</div><input id='attrs-5230ef05-9849-4414-b514-e0e6d9c5601c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5230ef05-9849-4414-b514-e0e6d9c5601c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c84057ed-0bd3-40ca-9216-a6c123116f56' class='xr-var-data-in' type='checkbox'><label for='data-c84057ed-0bd3-40ca-9216-a6c123116f56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.74816963, 0.74816963, 0.74816963, ..., 0.74816963, 0.74816963,\n",
       "        0.74816963],\n",
       "       [0.77641939, 0.77641939, 0.77641939, ..., 0.77641939, 0.77641939,\n",
       "        0.77641939],\n",
       "       [0.72921002, 0.72921002, 0.72921002, ..., 0.72921002, 0.72921002,\n",
       "        0.72921002],\n",
       "       [0.62939095, 0.62939095, 0.62939095, ..., 0.62939095, 0.62939095,\n",
       "        0.62939095]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 3 3 2 3 3 3 ... 3 2 3 3 3 3 2 3</div><input id='attrs-9aed652c-6ff1-44dc-a5a9-3abda9ff95d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9aed652c-6ff1-44dc-a5a9-3abda9ff95d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab5df051-bbd3-4d3b-9840-ba075d0572a2' class='xr-var-data-in' type='checkbox'><label for='data-ab5df051-bbd3-4d3b-9840-ba075d0572a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 3, 3, ..., 2, 3, 3],\n",
       "       [2, 2, 2, ..., 2, 2, 3],\n",
       "       [2, 3, 3, ..., 3, 3, 2],\n",
       "       [3, 2, 3, ..., 3, 2, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 7 7 7 3 7 7 7 ... 7 7 7 7 7 7 7 7</div><input id='attrs-0381a6eb-bcbf-434f-b9be-1e088d9d29c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0381a6eb-bcbf-434f-b9be-1e088d9d29c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6f159a7-f6b9-45e8-9350-c78b0584a962' class='xr-var-data-in' type='checkbox'><label for='data-c6f159a7-f6b9-45e8-9350-c78b0584a962' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 7, 7, ..., 3, 7, 7],\n",
       "       [3, 7, 3, ..., 3, 3, 7],\n",
       "       [3, 7, 7, ..., 7, 7, 3],\n",
       "       [7, 7, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-45955be8-a13f-4c21-847a-29cef637d76d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45955be8-a13f-4c21-847a-29cef637d76d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ebd0b2c-6428-412a-92b7-e8ec5c6dae8e' class='xr-var-data-in' type='checkbox'><label for='data-8ebd0b2c-6428-412a-92b7-e8ec5c6dae8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-280.2 -279.4 ... -284.5 -279.4</div><input id='attrs-19ab10a8-5931-435d-a2d8-48101dd57f32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19ab10a8-5931-435d-a2d8-48101dd57f32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-503c750e-1a99-4b18-b0a7-7227686331c7' class='xr-var-data-in' type='checkbox'><label for='data-503c750e-1a99-4b18-b0a7-7227686331c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-280.22409019, -279.42044087, -276.31169361, ..., -284.13322661,\n",
       "        -280.46439636, -279.95886089],\n",
       "       [-281.85439027, -280.10797729, -282.62894167, ..., -279.25820597,\n",
       "        -283.70027846, -283.58382615],\n",
       "       [-282.44308315, -280.8447651 , -283.83310088, ..., -279.2355776 ,\n",
       "        -282.39050522, -282.73592252],\n",
       "       [-279.71465388, -282.20212869, -283.71703407, ..., -285.60485591,\n",
       "        -284.45111374, -279.44787476]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>285.4 280.7 282.5 ... 285.7 283.1</div><input id='attrs-07a458e2-d7ce-4256-bf4d-324227b0fd82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07a458e2-d7ce-4256-bf4d-324227b0fd82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c2b681-79f1-40ec-9246-72a4a63694f7' class='xr-var-data-in' type='checkbox'><label for='data-62c2b681-79f1-40ec-9246-72a4a63694f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[285.36668373, 280.68692205, 282.48071633, ..., 285.56397967,\n",
       "        281.7809471 , 284.69464813],\n",
       "       [282.83881366, 283.99308837, 286.75076077, ..., 284.27689286,\n",
       "        284.59324685, 287.12767882],\n",
       "       [284.23776522, 285.22246711, 286.22752969, ..., 283.19614676,\n",
       "        283.70161901, 286.07414837],\n",
       "       [283.87489254, 285.48190399, 285.93860696, ..., 286.64935928,\n",
       "        285.65934025, 283.14995695]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7557815-8b79-42ea-97b1-dd405ef12912' class='xr-section-summary-in' type='checkbox'  ><label for='section-f7557815-8b79-42ea-97b1-dd405ef12912' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7bcec6a0-9561-4646-bdd3-5ba96a63ad8f' class='xr-index-data-in' type='checkbox'/><label for='index-7bcec6a0-9561-4646-bdd3-5ba96a63ad8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a43f86e8-a790-46a8-9e39-4f5515f7da26' class='xr-index-data-in' type='checkbox'/><label for='index-a43f86e8-a790-46a8-9e39-4f5515f7da26' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1864036-a3df-46eb-8d83-c546d6c3bc67' class='xr-section-summary-in' type='checkbox'  ><label for='section-e1864036-a3df-46eb-8d83-c546d6c3bc67' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.463167</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/5jmion4o</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta[2];\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta[ cid[i] ] * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior4bef3f0a-8525-4e80-bafc-954295f7f0e1\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_prior4bef3f0a-8525-4e80-bafc-954295f7f0e1\" class = \"xr-section-summary\">prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000, africa: 2, mu_dim_0: 170,\n",
       "                            log_lik_dim_0: 170, log_gdp_std_hat_dim_0: 170)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1 2 3\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "  * africa                 (africa) int64 0 1\n",
       "  * mu_dim_0               (mu_dim_0) int64 0 1 2 3 4 5 ... 165 166 167 168 169\n",
       "  * log_lik_dim_0          (log_lik_dim_0) int64 0 1 2 3 4 ... 166 167 168 169\n",
       "  * log_gdp_std_hat_dim_0  (log_gdp_std_hat_dim_0) int64 0 1 2 3 ... 167 168 169\n",
       "Data variables:\n",
       "    alpha                  (chain, draw, africa) float64 0.88 1.051 ... 1.022\n",
       "    beta                   (chain, draw, africa) float64 0.2193 ... -0.1622\n",
       "    sigma                  (chain, draw) float64 0.1212 0.131 ... 0.1209 0.1094\n",
       "    mu                     (chain, draw, mu_dim_0) float64 0.8632 ... 0.8728\n",
       "    log_lik                (chain, draw, log_lik_dim_0) float64 1.182 ... 1.206\n",
       "    log_gdp_std_hat        (chain, draw, log_gdp_std_hat_dim_0) float64 0.913...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.647322\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/5jmion4o\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f672b50f-5b27-4efc-9944-6e4e2abb00ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f672b50f-5b27-4efc-9944-6e4e2abb00ea' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>africa</span>: 2</li><li><span class='xr-has-index'>mu_dim_0</span>: 170</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 170</li><li><span class='xr-has-index'>log_gdp_std_hat_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad8bd21c-b87d-409d-80e9-6939563d33bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad8bd21c-b87d-409d-80e9-6939563d33bf' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-64674ad1-d598-4575-bf0f-f0d66c7c6e5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64674ad1-d598-4575-bf0f-f0d66c7c6e5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aff768e2-17ad-4aa3-b763-9922cc779921' class='xr-var-data-in' type='checkbox'><label for='data-aff768e2-17ad-4aa3-b763-9922cc779921' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-d08deebd-bd90-4172-a3a2-15a053c692dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d08deebd-bd90-4172-a3a2-15a053c692dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a04cd569-76de-47ef-b79d-7cd46ff9826d' class='xr-var-data-in' type='checkbox'><label for='data-a04cd569-76de-47ef-b79d-7cd46ff9826d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>africa</span></div><div class='xr-var-dims'>(africa)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-0321ef6f-f340-4229-9b49-2a574b64174f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0321ef6f-f340-4229-9b49-2a574b64174f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5051be06-4dc8-422b-ba37-93099b1a8fd8' class='xr-var-data-in' type='checkbox'><label for='data-5051be06-4dc8-422b-ba37-93099b1a8fd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-2da5d07f-1679-4286-9ea2-6ad5ee063689' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2da5d07f-1679-4286-9ea2-6ad5ee063689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77b0ada3-6b35-4c36-be25-a52d0098ea5f' class='xr-var-data-in' type='checkbox'><label for='data-77b0ada3-6b35-4c36-be25-a52d0098ea5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-ba61a223-3515-4604-a07a-f44658d285ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba61a223-3515-4604-a07a-f44658d285ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b5597e-7053-415b-901c-c4defe99bfaa' class='xr-var-data-in' type='checkbox'><label for='data-24b5597e-7053-415b-901c-c4defe99bfaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_hat_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-e9ee7f25-29c5-432c-90f6-0aa6e315388a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9ee7f25-29c5-432c-90f6-0aa6e315388a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb8202e3-28ab-4a6c-88e7-2b0840584c55' class='xr-var-data-in' type='checkbox'><label for='data-eb8202e3-28ab-4a6c-88e7-2b0840584c55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f64863f-8131-49f5-a03f-0c2322de9777' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f64863f-8131-49f5-a03f-0c2322de9777' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw, africa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.88 1.051 0.8771 ... 0.876 1.022</div><input id='attrs-2dd2d95b-34a5-4863-8d46-078cae1f134a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2dd2d95b-34a5-4863-8d46-078cae1f134a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2589de7c-a11d-401b-93ef-bdb5422f8fe4' class='xr-var-data-in' type='checkbox'><label for='data-2589de7c-a11d-401b-93ef-bdb5422f8fe4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.8799981 , 1.05065779],\n",
       "        [0.87713665, 1.04325154],\n",
       "        [0.87348815, 1.04485125],\n",
       "        ...,\n",
       "        [0.87525775, 1.0649068 ],\n",
       "        [0.92603814, 1.03278517],\n",
       "        [0.87511528, 1.05470343]],\n",
       "\n",
       "       [[0.85667038, 1.06896312],\n",
       "        [0.8684841 , 1.05543232],\n",
       "        [0.88009216, 1.05577594],\n",
       "        ...,\n",
       "        [0.88103403, 1.05999608],\n",
       "        [0.88668431, 1.05804938],\n",
       "        [0.8934676 , 1.04821274]],\n",
       "\n",
       "       [[0.86320487, 1.0615367 ],\n",
       "        [0.88711142, 1.0437661 ],\n",
       "        [0.87645157, 1.05434753],\n",
       "        ...,\n",
       "        [0.91249938, 1.02800977],\n",
       "        [0.86577335, 1.07185186],\n",
       "        [0.87611937, 1.05781191]],\n",
       "\n",
       "       [[0.86701879, 1.06110604],\n",
       "        [0.87139506, 1.04891813],\n",
       "        [0.90980155, 1.04566211],\n",
       "        ...,\n",
       "        [0.89693459, 1.04238248],\n",
       "        [0.89880551, 1.05136368],\n",
       "        [0.87604605, 1.02221448]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw, africa)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2193 -0.1387 ... 0.1464 -0.1622</div><input id='attrs-34744247-4f7a-4a60-ae5d-7250b11796a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34744247-4f7a-4a60-ae5d-7250b11796a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4874c508-483c-4815-9165-eb527e04f0be' class='xr-var-data-in' type='checkbox'><label for='data-4874c508-483c-4815-9165-eb527e04f0be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.21926906, -0.13874595],\n",
       "        [ 0.16738814, -0.0141583 ],\n",
       "        [ 0.33900366, -0.09472518],\n",
       "        ...,\n",
       "        [ 0.21318697, -0.12580214],\n",
       "        [ 0.14861179, -0.07904649],\n",
       "        [ 0.08399365, -0.02291149]],\n",
       "\n",
       "       [[ 0.14185605, -0.08482378],\n",
       "        [-0.0547438 , -0.17420287],\n",
       "        [ 0.19536518, -0.17603927],\n",
       "        ...,\n",
       "        [ 0.03362215, -0.1933    ],\n",
       "        [ 0.04585387, -0.19789593],\n",
       "        [ 0.10939905, -0.14815578]],\n",
       "\n",
       "       [[ 0.14183397, -0.04965893],\n",
       "        [ 0.23157524, -0.22574906],\n",
       "        [ 0.15343804, -0.0636724 ],\n",
       "        ...,\n",
       "        [ 0.08964895, -0.16182891],\n",
       "        [ 0.17608494, -0.13570401],\n",
       "        [ 0.12740187, -0.12255009]],\n",
       "\n",
       "       [[ 0.11241455, -0.10339285],\n",
       "        [ 0.11061567, -0.05462318],\n",
       "        [ 0.22438704, -0.14614973],\n",
       "        ...,\n",
       "        [ 0.07025921, -0.13957218],\n",
       "        [ 0.13794591, -0.09530278],\n",
       "        [ 0.1464107 , -0.16219026]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1212 0.131 ... 0.1209 0.1094</div><input id='attrs-047daf75-c77f-49aa-a395-6113f1d04ce1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-047daf75-c77f-49aa-a395-6113f1d04ce1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e5e5fe9-ebd9-4a28-a300-5f50827fea38' class='xr-var-data-in' type='checkbox'><label for='data-7e5e5fe9-ebd9-4a28-a300-5f50827fea38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.12120657, 0.13103159, 0.11016947, ..., 0.11075485, 0.12176888,\n",
       "        0.11407225],\n",
       "       [0.10600995, 0.11391783, 0.11305613, ..., 0.10055508, 0.10057636,\n",
       "        0.10525774],\n",
       "       [0.10893276, 0.10850748, 0.11185106, ..., 0.11659326, 0.10601317,\n",
       "        0.11926662],\n",
       "       [0.09931887, 0.10542284, 0.10944904, ..., 0.11180329, 0.12094873,\n",
       "        0.10941977]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8632 1.004 ... 0.8572 0.8728</div><input id='attrs-53d93bb5-8ff3-4bfa-a478-7d8b1f536f5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53d93bb5-8ff3-4bfa-a478-7d8b1f536f5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4fb1308-3904-471d-9ad0-80f122821f06' class='xr-var-data-in' type='checkbox'><label for='data-e4fb1308-3904-471d-9ad0-80f122821f06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.86319823, 1.00381665, 1.0632792 , ..., 0.89512341,\n",
       "         0.85170799, 0.87507736],\n",
       "        [0.86431178, 1.03847165, 1.04453949, ..., 0.88868319,\n",
       "         0.85554023, 0.87338021],\n",
       "        [0.84751451, 1.01287168, 1.05346819, ..., 0.89687283,\n",
       "         0.82974988, 0.86588039],\n",
       "        ...,\n",
       "        [0.85892387, 1.02243554, 1.07635074, ..., 0.88996351,\n",
       "         0.84775235, 0.87047351],\n",
       "        [0.91465186, 1.00609879, 1.03997586, ..., 0.93628947,\n",
       "         0.90686424, 0.92270306],\n",
       "        [0.86867988, 1.04696843, 1.05678764, ..., 0.88090921,\n",
       "         0.86427841, 0.87323033]],\n",
       "\n",
       "       [[0.84580171, 1.04032631, 1.07667936, ..., 0.8664557 ,\n",
       "         0.83836811, 0.85348691],\n",
       "        [0.87267844, 0.9966208 , 1.07127918, ..., 0.86470784,\n",
       "         0.87554715, 0.86971264],\n",
       "        [0.86512375, 0.99634444, 1.07178984, ..., 0.89356857,\n",
       "         0.85488614, 0.87570787],\n",
       "...\n",
       "        [0.9056307 , 0.97337575, 1.04273099, ..., 0.91868342,\n",
       "         0.90093287, 0.91048752],\n",
       "        [0.85228214, 1.0260377 , 1.08419656, ..., 0.87791979,\n",
       "         0.84305486, 0.86182173],\n",
       "        [0.86635814, 1.01643856, 1.06896003, ..., 0.88490762,\n",
       "         0.85968197, 0.87326028]],\n",
       "\n",
       "       [[0.85840586, 1.02620025, 1.07051147, ..., 0.87477322,\n",
       "         0.85251506, 0.86449604],\n",
       "        [0.86291996, 1.03047715, 1.05388709, ..., 0.8790254 ,\n",
       "         0.85712342, 0.86891268],\n",
       "        [0.89260956, 0.99632143, 1.05895703, ..., 0.9252799 ,\n",
       "         0.88085113, 0.90476597],\n",
       "        ...,\n",
       "        [0.89155149, 0.99526241, 1.05507906, ..., 0.90178111,\n",
       "         0.88786974, 0.89535786],\n",
       "        [0.88823643, 1.01918912, 1.06003317, ..., 0.9083211 ,\n",
       "         0.88100772, 0.89570979],\n",
       "        [0.86482842, 0.96745846, 1.03696857, ..., 0.88614555,\n",
       "         0.85715614, 0.87276037]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.182 1.139 0.8303 ... 1.051 1.206</div><input id='attrs-7a686186-4af4-4fcc-af6e-33764988fce1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a686186-4af4-4fcc-af6e-33764988fce1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40048cfe-dbcf-48e3-b3db-09fd0f3ce800' class='xr-var-data-in' type='checkbox'><label for='data-40048cfe-dbcf-48e3-b3db-09fd0f3ce800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 1.1820393 ,  1.13938961,  0.8303109 , ...,  0.09787357,\n",
       "          1.02100108,  1.12688395],\n",
       "        [ 1.10647169,  0.955125  ,  0.68183897, ...,  0.10931974,\n",
       "          0.95142574,  1.0538577 ],\n",
       "        [ 1.24409104,  1.1914198 ,  0.76261332, ..., -0.01100477,\n",
       "          1.1887603 ,  1.17234755],\n",
       "        ...,\n",
       "        [ 1.26388307,  1.14588255,  0.9519222 , ..., -0.10454219,\n",
       "          1.09969021,  1.18713108],\n",
       "        [ 1.14552552,  1.12905189,  0.64883444, ...,  0.54380592,\n",
       "          0.65221112,  1.18612124],\n",
       "        [ 1.24730823,  0.99226869,  0.79140774, ..., -0.18607086,\n",
       "          0.98528502,  1.17292883]],\n",
       "\n",
       "       [[ 1.27412312,  1.07118996,  0.96816979, ..., -0.59792608,\n",
       "          1.1786877 ,  1.13671538],\n",
       "        [ 1.25143334,  1.21421521,  0.90567957, ..., -0.44024966,\n",
       "          0.90868025,  1.16129702],\n",
       "        [ 1.25260737,  1.22189551,  0.9117377 , ..., -0.01775354,\n",
       "          1.04718556,  1.189001  ],\n",
       "...\n",
       "        [ 1.20541632,  1.22739159,  0.66877386, ...,  0.33866842,\n",
       "          0.70077761,  1.22771106],\n",
       "        [ 1.29178055,  1.15817115,  1.02557127, ..., -0.39160888,\n",
       "          1.15375171,  1.18188647],\n",
       "        [ 1.20118711,  1.11356031,  0.87460313, ..., -0.05424875,\n",
       "          0.98965888,  1.13523105]],\n",
       "\n",
       "       [[ 1.36747152,  1.19910525,  0.92568134, ..., -0.62878685,\n",
       "          1.1309997 ,  1.24216421],\n",
       "        [ 1.31815211,  1.13651266,  0.7626325 , ..., -0.38581898,\n",
       "          1.06991132,  1.21981706],\n",
       "        [ 1.28641429,  1.25176609,  0.81267934, ...,  0.3656366 ,\n",
       "          0.8769277 ,  1.28538204],\n",
       "        ...,\n",
       "        [ 1.26646864,  1.23484683,  0.77753335, ...,  0.08065864,\n",
       "          0.81494413,  1.25048791],\n",
       "        [ 1.19096623,  1.09217212,  0.80768604, ...,  0.25109225,\n",
       "          0.85137293,  1.17555811],\n",
       "        [ 1.28437418,  1.29331983,  0.5954114 , ..., -0.18585682,\n",
       "          1.05120516,  1.20591383]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std_hat</span></div><div class='xr-var-dims'>(chain, draw, log_gdp_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9133 1.06 0.9788 ... 0.922 0.7556</div><input id='attrs-1bbebdb6-f946-45c6-a44b-f7983cde8ce4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bbebdb6-f946-45c6-a44b-f7983cde8ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65547358-1fa5-47fd-9a6e-2b3a8f0ca54a' class='xr-var-data-in' type='checkbox'><label for='data-65547358-1fa5-47fd-9a6e-2b3a8f0ca54a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.91334535, 1.05970046, 0.97881659, ..., 0.83040664,\n",
       "         0.70359595, 1.05637005],\n",
       "        [0.71546209, 1.10500894, 1.30937776, ..., 0.92730361,\n",
       "         0.97688805, 0.97723416],\n",
       "        [0.95760535, 1.05530868, 1.1253216 , ..., 1.07533115,\n",
       "         0.81456514, 0.99479222],\n",
       "        ...,\n",
       "        [0.97217478, 1.09339357, 1.14342635, ..., 0.78115254,\n",
       "         0.95865994, 0.87245012],\n",
       "        [0.87386487, 1.11104821, 1.07002512, ..., 1.07274116,\n",
       "         1.02435039, 0.80489117],\n",
       "        [0.94640017, 0.904975  , 1.03796144, ..., 0.84437191,\n",
       "         0.79725304, 0.84477131]],\n",
       "\n",
       "       [[0.90745317, 1.03873503, 1.21806687, ..., 0.84733837,\n",
       "         0.69738243, 0.6929596 ],\n",
       "        [0.88046253, 0.88200227, 1.06160769, ..., 0.82347294,\n",
       "         0.99758956, 0.8216173 ],\n",
       "        [0.75864997, 1.08206765, 1.10102419, ..., 0.81515156,\n",
       "         0.72599967, 0.99645503],\n",
       "...\n",
       "        [0.7921488 , 0.98673542, 1.24539948, ..., 0.86309635,\n",
       "         0.69700953, 0.57322407],\n",
       "        [0.83691493, 0.86983215, 1.02955418, ..., 0.75501554,\n",
       "         0.99012119, 1.01352251],\n",
       "        [0.87615987, 1.1254726 , 1.03469804, ..., 0.86705399,\n",
       "         0.94522565, 0.82651532]],\n",
       "\n",
       "       [[0.81030272, 1.00444259, 1.00256717, ..., 1.05497432,\n",
       "         0.91606441, 1.06542145],\n",
       "        [0.58475272, 1.13150744, 1.17129389, ..., 0.934131  ,\n",
       "         0.93786   , 0.90262724],\n",
       "        [0.85820583, 1.03871165, 0.91714019, ..., 0.80313385,\n",
       "         0.85317569, 1.06026125],\n",
       "        ...,\n",
       "        [0.69520517, 0.98274854, 0.98247656, ..., 1.2069157 ,\n",
       "         0.79129649, 1.05382379],\n",
       "        [1.15529022, 0.980254  , 0.98750211, ..., 0.79397704,\n",
       "         0.82315815, 0.98375107],\n",
       "        [1.03550361, 0.82682667, 1.12376824, ..., 0.9299621 ,\n",
       "         0.92202406, 0.75560522]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-635cffd6-966f-4721-9523-da3860e3afe0' class='xr-section-summary-in' type='checkbox'  ><label for='section-635cffd6-966f-4721-9523-da3860e3afe0' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e5b9737-6b04-4648-b148-24416d15ad8e' class='xr-index-data-in' type='checkbox'/><label for='index-0e5b9737-6b04-4648-b148-24416d15ad8e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67228fdc-5416-404d-9733-bbfefcdbcf18' class='xr-index-data-in' type='checkbox'/><label for='index-67228fdc-5416-404d-9733-bbfefcdbcf18' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>africa</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7dc3562-7fc0-4806-b22c-f7024243380a' class='xr-index-data-in' type='checkbox'/><label for='index-b7dc3562-7fc0-4806-b22c-f7024243380a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;africa&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b626cbdf-26c5-4569-9dfe-b656a711d70b' class='xr-index-data-in' type='checkbox'/><label for='index-b626cbdf-26c5-4569-9dfe-b656a711d70b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4f67d6ea-695d-40dc-98a8-9511c33b7374' class='xr-index-data-in' type='checkbox'/><label for='index-4f67d6ea-695d-40dc-98a8-9511c33b7374' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;, length=170))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d025ebad-5d84-47b1-883b-f94aaae7df43' class='xr-index-data-in' type='checkbox'/><label for='index-d025ebad-5d84-47b1-883b-f94aaae7df43' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_hat_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ca0441f-dc77-43fe-a2d8-6fc9a8af51b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-9ca0441f-dc77-43fe-a2d8-6fc9a8af51b7' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.647322</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/5jmion4o</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta[2];\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta[ cid[i] ] * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
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       "            \n",
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       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 285.4 280.7 282.5 ... 285.7 283.1\n",
       "    acceptance_rate  (chain, draw) float64 0.9562 0.7774 ... 0.9232 0.8468\n",
       "    step_size        (chain, draw) float64 0.7482 0.7482 ... 0.6294 0.6294\n",
       "    tree_depth       (chain, draw) int64 2 3 3 3 2 3 3 3 2 ... 3 3 2 3 3 3 3 2 3\n",
       "    n_steps          (chain, draw) int64 3 7 7 7 3 7 7 7 3 ... 7 7 7 7 7 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -280.2 -279.4 ... -284.5 -279.4\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.696630\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/5jmion4o\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3ec75f46-54e3-4577-af15-a997c53f5633' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3ec75f46-54e3-4577-af15-a997c53f5633' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c3681b9a-b0b7-4c05-9a76-438788cb51ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3681b9a-b0b7-4c05-9a76-438788cb51ea' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-6fc3a0b9-b15d-4aa6-8cfe-afffc4836a80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fc3a0b9-b15d-4aa6-8cfe-afffc4836a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eae3d1e5-18d3-4e5d-b6c5-17b0d804aca5' class='xr-var-data-in' type='checkbox'><label for='data-eae3d1e5-18d3-4e5d-b6c5-17b0d804aca5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-2c4cf14b-a6ab-49d5-87a1-c7510202e9ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c4cf14b-a6ab-49d5-87a1-c7510202e9ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e0216d9-640b-4cd4-bfde-5dd73a2e3f02' class='xr-var-data-in' type='checkbox'><label for='data-6e0216d9-640b-4cd4-bfde-5dd73a2e3f02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44df8048-ca2e-4818-8d48-de791a64040e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44df8048-ca2e-4818-8d48-de791a64040e' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>285.4 280.7 282.5 ... 285.7 283.1</div><input id='attrs-740c1769-5c04-4f13-ad24-f55099ee45ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-740c1769-5c04-4f13-ad24-f55099ee45ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a29720-b438-4b7f-a7cf-c5d5a5aa8214' class='xr-var-data-in' type='checkbox'><label for='data-90a29720-b438-4b7f-a7cf-c5d5a5aa8214' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[285.36668373, 280.68692205, 282.48071633, ..., 285.56397967,\n",
       "        281.7809471 , 284.69464813],\n",
       "       [282.83881366, 283.99308837, 286.75076077, ..., 284.27689286,\n",
       "        284.59324685, 287.12767882],\n",
       "       [284.23776522, 285.22246711, 286.22752969, ..., 283.19614676,\n",
       "        283.70161901, 286.07414837],\n",
       "       [283.87489254, 285.48190399, 285.93860696, ..., 286.64935928,\n",
       "        285.65934025, 283.14995695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9562 0.7774 ... 0.9232 0.8468</div><input id='attrs-dd373d26-6052-4214-b349-6f87a14751fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd373d26-6052-4214-b349-6f87a14751fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aae0b587-cfa2-4396-ad29-87c7ab3b0976' class='xr-var-data-in' type='checkbox'><label for='data-aae0b587-cfa2-4396-ad29-87c7ab3b0976' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.9561964 , 0.77743986, 0.97084456, ..., 0.85410249, 0.74106279,\n",
       "        1.        ],\n",
       "       [0.87203886, 0.97887694, 0.96976632, ..., 0.69208485, 1.        ,\n",
       "        0.91256317],\n",
       "       [0.76677045, 0.81393291, 0.96968841, ..., 0.6303582 , 0.99270845,\n",
       "        1.        ],\n",
       "       [0.98556169, 0.95836686, 0.99178639, ..., 0.90313257, 0.92318978,\n",
       "        0.84679371]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7482 0.7482 ... 0.6294 0.6294</div><input id='attrs-87f669ea-ce3f-4a63-9a27-4d95252e1a18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87f669ea-ce3f-4a63-9a27-4d95252e1a18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-453c1499-ad54-4000-9a89-ee4fbf45839b' class='xr-var-data-in' type='checkbox'><label for='data-453c1499-ad54-4000-9a89-ee4fbf45839b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.74816963, 0.74816963, 0.74816963, ..., 0.74816963, 0.74816963,\n",
       "        0.74816963],\n",
       "       [0.77641939, 0.77641939, 0.77641939, ..., 0.77641939, 0.77641939,\n",
       "        0.77641939],\n",
       "       [0.72921002, 0.72921002, 0.72921002, ..., 0.72921002, 0.72921002,\n",
       "        0.72921002],\n",
       "       [0.62939095, 0.62939095, 0.62939095, ..., 0.62939095, 0.62939095,\n",
       "        0.62939095]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 3 3 2 3 3 3 ... 3 2 3 3 3 3 2 3</div><input id='attrs-da7c5af5-6926-45a4-91bd-e0902b04a9e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da7c5af5-6926-45a4-91bd-e0902b04a9e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c532920-f51a-451c-a5e9-3adc5eca18f2' class='xr-var-data-in' type='checkbox'><label for='data-9c532920-f51a-451c-a5e9-3adc5eca18f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 3, 3, ..., 2, 3, 3],\n",
       "       [2, 2, 2, ..., 2, 2, 3],\n",
       "       [2, 3, 3, ..., 3, 3, 2],\n",
       "       [3, 2, 3, ..., 3, 2, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 7 7 7 3 7 7 7 ... 7 7 7 7 7 7 7 7</div><input id='attrs-0c0fc226-a392-492a-994a-18990f02dc01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c0fc226-a392-492a-994a-18990f02dc01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd50aa92-1767-4645-a2c2-8740fb17e591' class='xr-var-data-in' type='checkbox'><label for='data-cd50aa92-1767-4645-a2c2-8740fb17e591' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 7, 7, ..., 3, 7, 7],\n",
       "       [3, 7, 3, ..., 3, 3, 7],\n",
       "       [3, 7, 7, ..., 7, 7, 3],\n",
       "       [7, 7, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-3e605a83-e2b5-403e-a6b0-19ccb9b58649' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e605a83-e2b5-403e-a6b0-19ccb9b58649' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e59b315-c08c-4fbf-a07a-dcea02feb006' class='xr-var-data-in' type='checkbox'><label for='data-6e59b315-c08c-4fbf-a07a-dcea02feb006' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-280.2 -279.4 ... -284.5 -279.4</div><input id='attrs-3f13d411-4468-47ae-8181-9eb5c3695a71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f13d411-4468-47ae-8181-9eb5c3695a71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd88eebe-0d2c-48f7-99fc-1ea22dde3c00' class='xr-var-data-in' type='checkbox'><label for='data-fd88eebe-0d2c-48f7-99fc-1ea22dde3c00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-280.22409019, -279.42044087, -276.31169361, ..., -284.13322661,\n",
       "        -280.46439636, -279.95886089],\n",
       "       [-281.85439027, -280.10797729, -282.62894167, ..., -279.25820597,\n",
       "        -283.70027846, -283.58382615],\n",
       "       [-282.44308315, -280.8447651 , -283.83310088, ..., -279.2355776 ,\n",
       "        -282.39050522, -282.73592252],\n",
       "       [-279.71465388, -282.20212869, -283.71703407, ..., -285.60485591,\n",
       "        -284.45111374, -279.44787476]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e89e9676-c93f-464d-97fc-d0f875e59a27' class='xr-section-summary-in' type='checkbox'  ><label for='section-e89e9676-c93f-464d-97fc-d0f875e59a27' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1dedf3b7-8e97-48ae-9233-dbb71c8bc4d0' class='xr-index-data-in' type='checkbox'/><label for='index-1dedf3b7-8e97-48ae-9233-dbb71c8bc4d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e002c7be-091f-4d19-a067-615e9f2d4e37' class='xr-index-data-in' type='checkbox'/><label for='index-e002c7be-091f-4d19-a067-615e9f2d4e37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf67b32d-15a5-40b0-9c16-29b18960ce81' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf67b32d-15a5-40b0-9c16-29b18960ce81' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.696630</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/5jmion4o</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] log_gdp_std;\n",
       "        vector[N] rugged_std;\n",
       "        array[N] int cid;  // Must be integer because this is index to alpha.\n",
       "        real rugged_std_average;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha[2];  //Can be used to real alpha[2] or array[2] int alpha;\n",
       "        real beta[2];\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        for (i in 1:N){\n",
       "            mu[i] = alpha[ cid[i] ] + beta[ cid[i] ] * (rugged_std[i] - rugged_std_average);\n",
       "        }\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Prioris\n",
       "        \n",
       "        alpha ~ normal(1, 0.1);\n",
       "        beta ~ normal(0, 0.3);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        // Likelihood\n",
       "        log_gdp_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] log_gdp_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(log_gdp_std[i] | mu[i], sigma);\n",
       "            log_gdp_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data40e532e6-5f68-4eaa-81ff-969da4d934a0\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data40e532e6-5f68-4eaa-81ff-969da4d934a0\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (log_gdp_std_dim_0: 170)\n",
       "Coordinates:\n",
       "  * log_gdp_std_dim_0  (log_gdp_std_dim_0) int64 0 1 2 3 4 ... 166 167 168 169\n",
       "Data variables:\n",
       "    log_gdp_std        (log_gdp_std_dim_0) float64 0.8797 0.9648 ... 0.9186\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:30.365835\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9ee4dcc3-ff64-4679-9086-dc321fded843' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ee4dcc3-ff64-4679-9086-dc321fded843' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>log_gdp_std_dim_0</span>: 170</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ad5e8fb-8869-4718-a137-5265c7e43b4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ad5e8fb-8869-4718-a137-5265c7e43b4c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_gdp_std_dim_0</span></div><div class='xr-var-dims'>(log_gdp_std_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 165 166 167 168 169</div><input id='attrs-7eacf97d-084e-43fa-bfab-06ddbf00d05c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7eacf97d-084e-43fa-bfab-06ddbf00d05c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae30e63b-cfe6-47d0-8e87-3c4793a7e804' class='xr-var-data-in' type='checkbox'><label for='data-ae30e63b-cfe6-47d0-8e87-3c4793a7e804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc34dc68-eed1-4cc7-afec-878401919b96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc34dc68-eed1-4cc7-afec-878401919b96' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_gdp_std</span></div><div class='xr-var-dims'>(log_gdp_std_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8797 0.9648 ... 0.781 0.9186</div><input id='attrs-1be93aa1-7930-4b79-bf86-c12055993c46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1be93aa1-7930-4b79-bf86-c12055993c46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a5fa985-3d8a-48e2-923c-b3600927ae9e' class='xr-var-data-in' type='checkbox'><label for='data-2a5fa985-3d8a-48e2-923c-b3600927ae9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.87971187, 0.9647547 , 1.1662705 , 1.10448536, 0.91490375,\n",
       "       1.0816501 , 1.19091827, 1.20635083, 0.92191149, 0.75522974,\n",
       "       1.19932025, 0.80615567, 0.81085767, 0.85700117, 1.02100708,\n",
       "       1.13604626, 1.14353554, 1.00673891, 0.99526755, 1.01882817,\n",
       "       0.91377709, 1.04445526, 1.13125035, 1.05074296, 0.82725418,\n",
       "       1.1992612 , 1.21101002, 1.07051814, 0.97168624, 0.86442499,\n",
       "       0.88532949, 0.76378895, 0.80593281, 1.02608907, 0.87809731,\n",
       "       0.99665294, 1.06397383, 1.16462671, 1.13188329, 1.19121349,\n",
       "       0.88528458, 1.02007148, 1.20538601, 1.0291909 , 1.0094337 ,\n",
       "       0.95380637, 0.96138451, 0.80021355, 1.17270317, 1.0785727 ,\n",
       "       0.7578708 , 1.19154684, 0.99882645, 1.19220721, 1.02371474,\n",
       "       1.19506889, 0.8851899 , 0.88595898, 0.89099694, 0.87527671,\n",
       "       0.78224406, 1.13047194, 1.14636797, 1.04818317, 0.97305583,\n",
       "       0.97590885, 1.19265328, 0.91891832, 1.07593003, 0.87995217,\n",
       "       1.11419863, 0.94113391, 0.91456917, 1.2124455 , 1.01797366,\n",
       "       1.20611401, 1.18348401, 1.1889489 , 0.96307105, 0.96922103,\n",
       "       1.19456727, 0.99007688, 0.81322267, 0.86325463, 0.8838469 ,\n",
       "       1.09495636, 1.13783355, 1.1362733 , 0.86399746, 0.97999287,\n",
       "       1.01374697, 0.96226885, 0.89940883, 1.06594471, 1.28736127,\n",
       "       1.05377081, 1.15413994, 0.95859462, 0.84465573, 0.78849201,\n",
       "       1.06962336, 1.0225778 , 0.78181112, 1.15206046, 0.86771734,\n",
       "       0.79561618, 0.8753453 , 1.0768738 , 0.74834587, 1.06805786,\n",
       "       1.0225421 , 1.17470978, 0.76969203, 0.79637985, 0.95042471,\n",
       "       1.20481088, 1.22579142, 0.84389598, 1.16049201, 1.10972919,\n",
       "       0.8879629 , 1.02458815, 0.99330541, 0.9746153 , 0.91014275,\n",
       "       1.08601201, 1.17525067, 1.15205757, 0.98901498, 1.18678542,\n",
       "       1.0192294 , 1.04110842, 0.81555521, 1.10640269, 0.87214242,\n",
       "       0.85345509, 1.18226546, 0.88406187, 0.72155551, 0.99007276,\n",
       "       1.09578033, 1.14281499, 1.19312592, 0.99227962, 1.15012638,\n",
       "       0.9491481 , 0.78790809, 0.85360006, 1.02676137, 0.78269215,\n",
       "       0.96362185, 1.0320481 , 1.0686262 , 1.02623744, 1.03100012,\n",
       "       0.73467768, 0.83663241, 0.97695723, 1.06613588, 1.2249729 ,\n",
       "       0.85987283, 1.00761228, 1.01508609, 0.89311399, 0.94658336,\n",
       "       0.99668099, 0.78303247, 1.0743655 , 0.78096666, 0.91858918])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1166dcff-70af-4208-a173-5e287cd7e0f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-1166dcff-70af-4208-a173-5e287cd7e0f3' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>log_gdp_std_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-75d052ec-2ac3-4c76-a06a-7e32e327222e' class='xr-index-data-in' type='checkbox'/><label for='index-75d052ec-2ac3-4c76-a06a-7e32e327222e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_gdp_std_dim_0&#x27;, length=170))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6161c6a9-3427-498d-a71a-57b31570c4de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6161c6a9-3427-498d-a71a-57b31570c4de' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:30.365835</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> sample_stats_prior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stan_data4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b2b901",
   "metadata": {},
   "source": [
    "### R Code 8.16 - XXX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9264bc75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# az.plot_loo_pit(stan_data4, y='log_gdp_std')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca43f550",
   "metadata": {},
   "source": [
    "### R Code 8.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "acab3752",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract 200 samples from arviz-fit to numpy\n",
    "params_post = az.extract(stan_data4.posterior, num_samples=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fafc1c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rugged_seq = np.linspace(0, 1, 30)\n",
    "\n",
    "log_gdp_mean_africa = []\n",
    "log_gdp_hdi_africa = []\n",
    "\n",
    "log_gdp_mean_not_africa = []\n",
    "log_gdp_hdi_not_africa = []\n",
    "\n",
    "# Calculation posterior mean and interval HDI\n",
    "for i in range(len(rugged_seq)):\n",
    "        log_gdp_africa = params_post.alpha.sel(africa=0) + params_post.beta.sel(africa=0).values * rugged_seq[i]\n",
    "        log_gdp_mean_africa.append(np.mean(log_gdp_africa.values))\n",
    "        log_gdp_hdi_africa.append(az.hdi(log_gdp_africa.values, hdi_prob=0.89))\n",
    "        \n",
    "        log_gdp_not_africa = params_post.alpha.sel(africa=1) + params_post.beta.sel(africa=1).values * rugged_seq[i]\n",
    "        log_gdp_mean_not_africa.append(np.mean(log_gdp_not_africa.values))\n",
    "        log_gdp_hdi_not_africa.append(az.hdi(log_gdp_not_africa.values, hdi_prob=0.89))\n",
    "        \n",
    "log_gdp_hdi_africa = np.array(log_gdp_hdi_africa)\n",
    "log_gdp_hdi_not_africa = np.array(log_gdp_hdi_not_africa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "676ee5fc",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1224x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(17, 8))\n",
    "\n",
    "gs = GridSpec(1, 2)\n",
    "\n",
    "# Africa plots\n",
    "ax_africa = fig.add_subplot(gs[0])\n",
    "\n",
    "ax_africa.plot(rugged_seq, log_gdp_mean_africa, c='blue')\n",
    "ax_africa.fill_between(rugged_seq, log_gdp_hdi_africa[:,0], log_gdp_hdi_africa[:,1], color='blue', alpha=0.3)\n",
    "ax_africa.plot(ddf.loc[ddf.cid == 1,'rugged_std'], ddf.loc[ddf.cid==1, 'log_gdp_std'], 'o', markerfacecolor='blue', color='gray')\n",
    "\n",
    "ax_africa.set_title('African Nations')\n",
    "ax_africa.set_xlabel('ruggedness (standardized)')\n",
    "ax_africa.set_ylabel('log GDP (as proportion of mean)')\n",
    "ax_africa.text(0.7, 0.8, 'Africa',  fontsize='xx-large', color='darkblue')\n",
    "\n",
    "\n",
    "# Non africa plots\n",
    "ax_not_africa = fig.add_subplot(gs[1])\n",
    "\n",
    "ax_not_africa.plot(rugged_seq, log_gdp_mean_not_africa, c='black')\n",
    "ax_not_africa.fill_between(rugged_seq, log_gdp_hdi_not_africa[:,0], log_gdp_hdi_not_africa[:,1], color='darkgray', alpha=0.3)\n",
    "ax_not_africa.plot(ddf.loc[ddf.cid == 2,'rugged_std'], ddf.loc[ddf.cid==2, 'log_gdp_std'], 'o', markerfacecolor='none', color='black')\n",
    "\n",
    "ax_not_africa.set_title('Non-African Nations')\n",
    "ax_not_africa.set_xlabel('ruggedness (standardized)')\n",
    "ax_not_africa.set_ylabel('log GDP (as proportion of mean)')\n",
    "ax_not_africa.text(0.7, 1.1, 'Not Africa', fontsize='xx-large', color='black')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b3f483",
   "metadata": {},
   "source": [
    "### R Code 8.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6f24f164",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_gdp_delta_mean = []\n",
    "log_gdp_delta_hdi = []\n",
    "\n",
    "# Calculation posterior mean and interval HDI\n",
    "for i in range(len(rugged_seq)):\n",
    "        log_gdp_africa = params_post.alpha.sel(africa=0) + params_post.beta.sel(africa=0).values * rugged_seq[i]\n",
    "        log_gdp_not_africa = params_post.alpha.sel(africa=1) + params_post.beta.sel(africa=1).values * rugged_seq[i]\n",
    "        \n",
    "        log_gdp_delta = log_gdp_africa - log_gdp_not_africa\n",
    "\n",
    "        log_gdp_delta_mean.append(np.mean(log_gdp_delta.values))\n",
    "        log_gdp_delta_hdi.append(az.hdi(log_gdp_delta.values, hdi_prob=0.89))\n",
    "        \n",
    "log_gdp_delta_hdi = np.array(log_gdp_delta_hdi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "295992c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(17, 8))\n",
    "\n",
    "gs = GridSpec(1, 1)\n",
    "\n",
    "# Africa delta log GDP plots\n",
    "ax_delta = fig.add_subplot(gs[0])\n",
    "\n",
    "ax_delta.plot(rugged_seq, log_gdp_delta_mean, c='black')\n",
    "ax_delta.fill_between(rugged_seq, log_gdp_delta_hdi[:,0], log_gdp_delta_hdi[:,1], color='blue', alpha=0.3)\n",
    "\n",
    "ax_delta.set_title('Delta log GDP')\n",
    "ax_delta.set_xlabel('ruggedness (standardized)')\n",
    "ax_delta.set_ylabel('expected difference log GDP')\n",
    "\n",
    "ax_delta.text(0.0, 0.02, 'Africa higher GDP',  fontsize='xx-large', color='darkblue')\n",
    "ax_delta.text(0.0, -0.03, 'Africa lower GDP',  fontsize='xx-large', color='darkblue')\n",
    "ax_delta.axhline(y=0, ls='--', color='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14095b65",
   "metadata": {},
   "source": [
    "### R Code 8.19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "229a72f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bed</th>\n",
       "      <th>water</th>\n",
       "      <th>shade</th>\n",
       "      <th>blooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>c</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>135.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>c</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>304.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>249.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>134.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bed  water  shade  blooms\n",
       "22   c    2.0    2.0  135.92\n",
       "23   c    2.0    3.0   90.66\n",
       "24   c    3.0    1.0  304.52\n",
       "25   c    3.0    2.0  249.33\n",
       "26   c    3.0    3.0  134.59"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/tulips.csv', sep=';',\n",
    "                 dtype={\n",
    "                     'bed': 'category',  # cluster of plants from same section of the greenhouse\n",
    "                     'water': 'float',  # Predictor: Soil moisture - (1) low and (3) high \n",
    "                     'shade': 'float',  # Predictor: Light exposure - (1) high and (3) low\n",
    "                     'blooms': 'float',  # What we wish to predict\n",
    "                 })\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "22b641ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bed\n",
       "count   27\n",
       "unique   3\n",
       "top      a\n",
       "freq     9"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ca001be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>water</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.832050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shade</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.832050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blooms</th>\n",
       "      <td>27.0</td>\n",
       "      <td>128.993704</td>\n",
       "      <td>92.683923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.115</td>\n",
       "      <td>111.04</td>\n",
       "      <td>190.3</td>\n",
       "      <td>361.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count        mean        std  min     25%     50%    75%     max\n",
       "water    27.0    2.000000   0.832050  1.0   1.000    2.00    3.0    3.00\n",
       "shade    27.0    2.000000   0.832050  1.0   1.000    2.00    3.0    3.00\n",
       "blooms   27.0  128.993704  92.683923  0.0  71.115  111.04  190.3  361.66"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e74fe2",
   "metadata": {},
   "source": [
    "### R Code 8.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9574d9b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['blooms_std'] = df['blooms'] / df['blooms'].max()\n",
    "df['water_cent'] = df['water'] - df['water'].mean()\n",
    "df['shade_cent'] = df['shade'] - df['shade'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3fdcc4",
   "metadata": {},
   "source": [
    "### R Code 8.21"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe0e20b",
   "metadata": {},
   "source": [
    "$$ \\alpha \\sim Normal(0.5, 1) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc828105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.628"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = np.random.normal(0.5, 1, 1000)\n",
    "\n",
    "alphas_true = np.any([alpha < 0, alpha > 1], axis=0)  # If (alpha < 0) or (alpha > 1) then True else False\n",
    "\n",
    "np.sum(alphas_true) / len(alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f28ec89",
   "metadata": {},
   "source": [
    "### R Code 8.22"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e40f6066",
   "metadata": {},
   "source": [
    "$$ \\alpha \\sim Normal(0.5, 0.25) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e919408f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.053"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = np.random.normal(0.5, 0.25, 1000)\n",
    "\n",
    "alphas_true = np.any([alpha < 0, alpha > 1], axis=0)  # If (alpha < 0) or (alpha > 1) then True else False\n",
    "\n",
    "np.sum(alphas_true) / len(alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90e3823",
   "metadata": {},
   "source": [
    "### R Code 8.23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0ef505b5",
   "metadata": {
    "scrolled": true,
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/tmp/httpstan_v58v6sbi/model_elkgjo5c.stan', line 30, column 8 to column 39)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 1.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] blooms_std;\n",
    "        vector[N] water_cent;\n",
    "        vector[N] shade_cent;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta_w;\n",
    "        real beta_s;\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta_w * water_cent + beta_s * shade_cent;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Priori\n",
    "        \n",
    "        alpha ~ normal(0.5, 0.25);\n",
    "        beta_w ~ normal(0, 0.25);\n",
    "        beta_s ~ normal(0, 0.25);\n",
    "        sigma ~ exponential(1);\n",
    "        \n",
    "        blooms_std ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;\n",
    "        vector[N] blooms_std_hat;\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
    "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(df),\n",
    "    'blooms_std': df.blooms_std.values,   \n",
    "    'water_cent': df.water_cent.values,\n",
    "    'shade_cent': df.shade_cent.values,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model, data=data)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e7993cb6",
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   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (chain: 4, draw: 1000, mu_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 0 1 2 3\n",
       "  * draw      (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * mu_dim_0  (mu_dim_0) int64 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    alpha     (chain, draw) float64 0.3615 0.3282 0.3282 ... 0.3661 0.3922 0.34\n",
       "    beta_w    (chain, draw) float64 0.2235 0.2231 0.2231 ... 0.2044 0.2055\n",
       "    beta_s    (chain, draw) float64 -0.08126 -0.07431 ... -0.1841 -0.04058\n",
       "    sigma     (chain, draw) float64 0.1818 0.1567 0.1567 ... 0.1719 0.1567\n",
       "    mu        (chain, draw, mu_dim_0) float64 0.2193 0.1381 ... 0.5455 0.505\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.510826\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/elkgjo5c\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cffa8056-ab12-4ba9-86fc-a82e36160267' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cffa8056-ab12-4ba9-86fc-a82e36160267' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-67fe3380-8b6f-4977-b1d6-03d2c0356b72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67fe3380-8b6f-4977-b1d6-03d2c0356b72' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-66e08788-37f4-4054-b965-d0ed93b942d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66e08788-37f4-4054-b965-d0ed93b942d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1fafcc6-ba85-40b6-87cb-9ba11e391d54' class='xr-var-data-in' type='checkbox'><label for='data-f1fafcc6-ba85-40b6-87cb-9ba11e391d54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-7f590a79-6f0d-459d-9e13-ce5a1f6c8465' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f590a79-6f0d-459d-9e13-ce5a1f6c8465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7594bcca-b11e-42d6-8578-2b666f83b983' class='xr-var-data-in' type='checkbox'><label for='data-7594bcca-b11e-42d6-8578-2b666f83b983' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-d4f7918d-ac12-4067-a01d-6959fa6d904a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4f7918d-ac12-4067-a01d-6959fa6d904a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd1a1642-a515-4860-a82f-781939fe901c' class='xr-var-data-in' type='checkbox'><label for='data-cd1a1642-a515-4860-a82f-781939fe901c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f604f416-c744-48d0-aaf9-02d2f55912c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f604f416-c744-48d0-aaf9-02d2f55912c7' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3615 0.3282 ... 0.3922 0.34</div><input id='attrs-ceddcf5d-d7b1-4e83-aed2-b9a444333247' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ceddcf5d-d7b1-4e83-aed2-b9a444333247' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d2ba07f-fe0d-44e6-a065-223f39aa9de2' class='xr-var-data-in' type='checkbox'><label for='data-9d2ba07f-fe0d-44e6-a065-223f39aa9de2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.36151912, 0.32815769, 0.32815769, ..., 0.398854  , 0.40135532,\n",
       "        0.34068593],\n",
       "       [0.38940917, 0.36037667, 0.22408745, ..., 0.3180109 , 0.40176888,\n",
       "        0.28706318],\n",
       "       [0.36522773, 0.36000492, 0.37166953, ..., 0.39080708, 0.33868423,\n",
       "        0.3814309 ],\n",
       "       [0.35703264, 0.36576697, 0.35507678, ..., 0.36614084, 0.39216189,\n",
       "        0.34001226]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_w</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2235 0.2231 ... 0.2044 0.2055</div><input id='attrs-66b181e5-973a-4fd9-adf4-1f7d8fe5f00b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66b181e5-973a-4fd9-adf4-1f7d8fe5f00b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e03d25b3-49a9-4e3c-a984-d5ff73bebdc2' class='xr-var-data-in' type='checkbox'><label for='data-e03d25b3-49a9-4e3c-a984-d5ff73bebdc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2234682 , 0.22313142, 0.22313142, ..., 0.24644808, 0.23838356,\n",
       "        0.27217199],\n",
       "       [0.18162366, 0.258444  , 0.14405701, ..., 0.27981642, 0.15127777,\n",
       "        0.24238516],\n",
       "       [0.16660002, 0.24548443, 0.18937575, ..., 0.1784585 , 0.2471408 ,\n",
       "        0.14368408],\n",
       "       [0.19808622, 0.22830251, 0.25519313, ..., 0.16415032, 0.20440446,\n",
       "        0.20553722]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.08126 -0.07431 ... -0.04058</div><input id='attrs-72f7f970-eb05-442a-a8b7-a0d7ee5cef02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72f7f970-eb05-442a-a8b7-a0d7ee5cef02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d744ba0f-a7dc-4650-ad2e-42309702a9a1' class='xr-var-data-in' type='checkbox'><label for='data-d744ba0f-a7dc-4650-ad2e-42309702a9a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.08125679, -0.07430747, -0.07430747, ..., -0.01521865,\n",
       "        -0.15071864, -0.18597174],\n",
       "       [-0.13930466, -0.1461497 , -0.21153582, ..., -0.05536522,\n",
       "        -0.09621488, -0.06535812],\n",
       "       [-0.06508399, -0.16381473, -0.06090548, ..., -0.10267189,\n",
       "        -0.11255645, -0.11444698],\n",
       "       [-0.13711154, -0.084861  , -0.06737803, ..., -0.14948422,\n",
       "        -0.18413257, -0.04057832]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1818 0.1567 ... 0.1719 0.1567</div><input id='attrs-f196eb6c-03a1-4e06-9047-874d870f8352' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f196eb6c-03a1-4e06-9047-874d870f8352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93eabf80-f300-4dda-af1e-e328151e45b5' class='xr-var-data-in' type='checkbox'><label for='data-93eabf80-f300-4dda-af1e-e328151e45b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18179702, 0.15674123, 0.15674123, ..., 0.19407449, 0.21766417,\n",
       "        0.1953823 ],\n",
       "       [0.17956849, 0.26526528, 0.19453234, ..., 0.21582145, 0.15505213,\n",
       "        0.22309311],\n",
       "       [0.15463377, 0.18767444, 0.16014579, ..., 0.20987668, 0.1392491 ,\n",
       "        0.21168626],\n",
       "       [0.13776296, 0.2037366 , 0.17394901, ..., 0.15948283, 0.17187688,\n",
       "        0.15665806]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2193 0.1381 ... 0.5455 0.505</div><input id='attrs-99afdcb6-82c3-4450-a026-7067b4dc1bef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99afdcb6-82c3-4450-a026-7067b4dc1bef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c912bbf4-edee-4905-a0d8-78d5f7fe9781' class='xr-var-data-in' type='checkbox'><label for='data-c912bbf4-edee-4905-a0d8-78d5f7fe9781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.21930771,  0.13805092,  0.05679413, ...,  0.66624411,\n",
       "          0.58498732,  0.50373053],\n",
       "        [ 0.17933374,  0.10502627,  0.0307188 , ...,  0.62559657,\n",
       "          0.55128911,  0.47698164],\n",
       "        [ 0.17933374,  0.10502627,  0.0307188 , ...,  0.62559657,\n",
       "          0.55128911,  0.47698164],\n",
       "        ...,\n",
       "        [ 0.16762457,  0.15240592,  0.13718726, ...,  0.66052074,\n",
       "          0.64530208,  0.63008343],\n",
       "        [ 0.31369039,  0.16297176,  0.01225312, ...,  0.79045752,\n",
       "          0.63973888,  0.48902024],\n",
       "        [ 0.25448567,  0.06851393, -0.11745781, ...,  0.79882966,\n",
       "          0.61285792,  0.42688618]],\n",
       "\n",
       "       [[ 0.34709017,  0.20778551,  0.06848084, ...,  0.71033749,\n",
       "          0.57103283,  0.43172817],\n",
       "        [ 0.24808237,  0.10193267, -0.04421703, ...,  0.76497037,\n",
       "          0.61882067,  0.47267097],\n",
       "        [ 0.29156627,  0.08003045, -0.13150537, ...,  0.57968028,\n",
       "          0.36814446,  0.15660864],\n",
       "...\n",
       "        [ 0.31502047,  0.21234858,  0.10967668, ...,  0.67193747,\n",
       "          0.56926558,  0.46659368],\n",
       "        [ 0.20409988,  0.09154342, -0.02101303, ...,  0.69838148,\n",
       "          0.58582503,  0.47326857],\n",
       "        [ 0.35219379,  0.23774682,  0.12329984, ...,  0.63956196,\n",
       "          0.52511499,  0.41066801]],\n",
       "\n",
       "       [[ 0.29605796,  0.15894642,  0.02183489, ...,  0.6922304 ,\n",
       "          0.55511887,  0.41800733],\n",
       "        [ 0.22232546,  0.13746446,  0.05260346, ...,  0.67893049,\n",
       "          0.59406949,  0.50920849],\n",
       "        [ 0.16726167,  0.09988364,  0.03250561, ...,  0.67764794,\n",
       "          0.61026991,  0.54289188],\n",
       "        ...,\n",
       "        [ 0.35147474,  0.20199052,  0.05250631, ...,  0.67977538,\n",
       "          0.53029116,  0.38080695],\n",
       "        [ 0.37189   ,  0.18775743,  0.00362486, ...,  0.78069892,\n",
       "          0.59656635,  0.41243378],\n",
       "        [ 0.17505336,  0.13447504,  0.09389672, ...,  0.58612781,\n",
       "          0.54554949,  0.50497117]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3948d532-55dc-49ea-bae4-f0bf7696213b' class='xr-section-summary-in' type='checkbox'  ><label for='section-3948d532-55dc-49ea-bae4-f0bf7696213b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-97fd707b-b156-4f0d-bddb-eaaf79012cdd' class='xr-index-data-in' type='checkbox'/><label for='index-97fd707b-b156-4f0d-bddb-eaaf79012cdd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-595df030-eaaa-40af-b187-0331b05a956e' class='xr-index-data-in' type='checkbox'/><label for='index-595df030-eaaa-40af-b187-0331b05a956e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b9bbb7c-7fac-4ecb-a0d4-bd12e60c40c7' class='xr-index-data-in' type='checkbox'/><label for='index-9b9bbb7c-7fac-4ecb-a0d4-bd12e60c40c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63037846-55b1-4cab-ad4c-328ca15d618e' class='xr-section-summary-in' type='checkbox'  ><label for='section-63037846-55b1-4cab-ad4c-328ca15d618e' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.510826</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/elkgjo5c</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
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       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (chain: 4, draw: 1000, blooms_std_hat_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain                 (chain) int64 0 1 2 3\n",
       "  * draw                  (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * blooms_std_hat_dim_0  (blooms_std_hat_dim_0) int64 0 1 2 3 4 ... 23 24 25 26\n",
       "Data variables:\n",
       "    blooms_std_hat        (chain, draw, blooms_std_hat_dim_0) float64 0.1343 ...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.654050\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-30490e83-09b5-4c69-80b0-56ef9b554c19' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-30490e83-09b5-4c69-80b0-56ef9b554c19' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>blooms_std_hat_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6727372e-2d26-4628-844d-08e647a3b078' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6727372e-2d26-4628-844d-08e647a3b078' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-fa9519de-b789-4f32-b896-5d50a7607ee9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa9519de-b789-4f32-b896-5d50a7607ee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-851fa066-0251-4459-8bdb-484b411699e9' class='xr-var-data-in' type='checkbox'><label for='data-851fa066-0251-4459-8bdb-484b411699e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-755b2b22-59a1-4b95-a512-4d472cc2cd06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-755b2b22-59a1-4b95-a512-4d472cc2cd06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55c9d816-e63d-4792-9dff-ce8c88d6eb8f' class='xr-var-data-in' type='checkbox'><label for='data-55c9d816-e63d-4792-9dff-ce8c88d6eb8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>blooms_std_hat_dim_0</span></div><div class='xr-var-dims'>(blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-606f1de1-054e-416d-b5c2-850fcca1f5f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-606f1de1-054e-416d-b5c2-850fcca1f5f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7261b9d2-852b-416b-9a99-f3913138a41c' class='xr-var-data-in' type='checkbox'><label for='data-7261b9d2-852b-416b-9a99-f3913138a41c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-25faaa51-f643-4f78-bce6-8112f7d10fea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25faaa51-f643-4f78-bce6-8112f7d10fea' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blooms_std_hat</span></div><div class='xr-var-dims'>(chain, draw, blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1343 0.5943 ... 0.3915 0.7434</div><input id='attrs-838eac7a-5a9f-434b-8750-bf24eab53e63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-838eac7a-5a9f-434b-8750-bf24eab53e63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11e39b0a-7b52-48ca-8e64-a24ae5952882' class='xr-var-data-in' type='checkbox'><label for='data-11e39b0a-7b52-48ca-8e64-a24ae5952882' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.13431731,  0.59425404,  0.01385853, ...,  0.66095709,\n",
       "          0.60488816,  0.20570972],\n",
       "        [ 0.17186603, -0.27925228,  0.0762902 , ...,  0.41903908,\n",
       "          0.67828193,  0.7832615 ],\n",
       "        [ 0.32582762,  0.13745404, -0.12311134, ...,  0.60691657,\n",
       "          0.74174309,  0.55835433],\n",
       "        ...,\n",
       "        [ 0.4789924 ,  0.12740402,  0.30143615, ...,  0.62751355,\n",
       "          0.76821062,  0.6914642 ],\n",
       "        [ 0.19186969,  0.26657567, -0.2792809 , ...,  1.06077298,\n",
       "          0.3942048 ,  0.62214776],\n",
       "        [ 0.28479492, -0.15423803,  0.17273948, ...,  0.42106936,\n",
       "          0.40280386,  0.98798013]],\n",
       "\n",
       "       [[ 0.32413327,  0.37987283,  0.03563597, ...,  0.38697192,\n",
       "          0.58961431,  0.40477897],\n",
       "        [ 0.2183097 ,  0.32593148, -0.50498804, ...,  0.95798906,\n",
       "          0.96302477,  0.56147575],\n",
       "        [ 0.28305719,  0.16280085, -0.34164725, ...,  0.80950041,\n",
       "          0.2899334 ,  0.44065836],\n",
       "...\n",
       "        [ 0.21929111,  0.63570024, -0.23934634, ...,  0.37549196,\n",
       "          0.72854746,  0.40837225],\n",
       "        [ 0.31968915, -0.20197696, -0.02666252, ...,  0.86668474,\n",
       "          0.76436461,  0.51641614],\n",
       "        [ 0.65702522,  0.09785624,  0.11138776, ...,  0.633106  ,\n",
       "          0.70392453,  0.25061156]],\n",
       "\n",
       "       [[ 0.16423277, -0.04556628, -0.02973752, ...,  0.67547891,\n",
       "          0.53918786,  0.63141729],\n",
       "        [ 0.01233017,  0.14773807, -0.14854822, ...,  0.8450887 ,\n",
       "          0.63403973,  0.30292474],\n",
       "        [ 0.15729091,  0.03893416,  0.26528013, ...,  0.52734266,\n",
       "          0.67691094,  0.47492956],\n",
       "        ...,\n",
       "        [ 0.6594963 ,  0.43112621,  0.00899109, ...,  0.53530003,\n",
       "          0.54954733,  0.33535696],\n",
       "        [ 0.53931735,  0.1930282 , -0.17342449, ...,  0.56710126,\n",
       "          0.50395754,  0.6162502 ],\n",
       "        [ 0.12728881,  0.39542413,  0.24333331, ...,  0.63115714,\n",
       "          0.39148701,  0.74344369]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8bc771a-cca5-4363-8dea-4dc737c12a06' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8bc771a-cca5-4363-8dea-4dc737c12a06' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3efb47a4-beff-42f2-9a17-4d93963ebb96' class='xr-index-data-in' type='checkbox'/><label for='index-3efb47a4-beff-42f2-9a17-4d93963ebb96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2e85812a-5af8-45eb-970c-b5ce5380ec54' class='xr-index-data-in' type='checkbox'/><label for='index-2e85812a-5af8-45eb-970c-b5ce5380ec54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>blooms_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f2bd151-cc27-49c0-96ad-31b166db1fc1' class='xr-index-data-in' type='checkbox'/><label for='index-9f2bd151-cc27-49c0-96ad-31b166db1fc1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;blooms_std_hat_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce5a9f0a-b8ce-466f-b65f-ffc598cb0e59' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce5a9f0a-b8ce-466f-b65f-ffc598cb0e59' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.654050</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
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       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (chain: 4, draw: 1000, log_lik_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain          (chain) int64 0 1 2 3\n",
       "  * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_lik_dim_0  (log_lik_dim_0) int64 0 1 2 3 4 5 6 ... 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    log_lik        (chain, draw, log_lik_dim_0) float64 0.05831 ... 0.5753\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.609401\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1e50540b-975f-411c-abda-e7c58b68530c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1e50540b-975f-411c-abda-e7c58b68530c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cd104256-e8e9-4905-ab92-ba714ec993be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd104256-e8e9-4905-ab92-ba714ec993be' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-2db5a10f-8950-4dc2-b017-ce54ebb81aa5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2db5a10f-8950-4dc2-b017-ce54ebb81aa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5045f3e5-3a6a-4d87-954a-e74f6cad3f8a' class='xr-var-data-in' type='checkbox'><label for='data-5045f3e5-3a6a-4d87-954a-e74f6cad3f8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-1ab780ad-fcfa-42ea-be8c-74c343341443' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ab780ad-fcfa-42ea-be8c-74c343341443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efc4772a-8aa3-4eac-bf7a-1a2272a61889' class='xr-var-data-in' type='checkbox'><label for='data-efc4772a-8aa3-4eac-bf7a-1a2272a61889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-365fe197-f55c-4050-99ff-bb54d4bd0dcf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-365fe197-f55c-4050-99ff-bb54d4bd0dcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a44f6f0e-a273-49fd-aef1-44eeb646ea48' class='xr-var-data-in' type='checkbox'><label for='data-a44f6f0e-a273-49fd-aef1-44eeb646ea48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cebb1ee3-2354-4e4c-9623-1d950f5414de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cebb1ee3-2354-4e4c-9623-1d950f5414de' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05831 0.4976 ... 0.5131 0.5753</div><input id='attrs-1288dd21-742f-4118-944e-ab74925bac62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1288dd21-742f-4118-944e-ab74925bac62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b480e09d-461a-4361-a11e-d2cc84be48d4' class='xr-var-data-in' type='checkbox'><label for='data-b480e09d-461a-4361-a11e-d2cc84be48d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.05830657,  0.49760574, -0.16138181, ...,  0.3185704 ,\n",
       "          0.62098075,  0.52397977],\n",
       "        [ 0.27969359,  0.70972927, -0.619584  , ..., -0.01892029,\n",
       "          0.54599227,  0.71053968],\n",
       "        [ 0.27969359,  0.70972927, -0.619584  , ..., -0.01892029,\n",
       "          0.54599227,  0.71053968],\n",
       "        ...,\n",
       "        [ 0.34757501,  0.4122298 ,  0.3376438 , ...,  0.28333732,\n",
       "          0.69475471, -0.16263707],\n",
       "        [-0.43261763,  0.32556486, -0.31115729, ...,  0.57781984,\n",
       "          0.57983146,  0.46170485],\n",
       "        [-0.13439604,  0.65237525, -1.6462248 , ...,  0.68944129,\n",
       "          0.63711356,  0.6746098 ]],\n",
       "\n",
       "       [[-1.06981528,  0.12877598, -0.0841304 , ...,  0.52943141,\n",
       "          0.58098762,  0.74321033],\n",
       "        [-0.02923529,  0.33425582, -0.46857385, ...,  0.3659171 ,\n",
       "          0.3726851 ,  0.33627968],\n",
       "        [-0.40499163,  0.6335937 , -1.82271394, ..., -0.19100204,\n",
       "         -0.6454212 ,  0.10441687],\n",
       "...\n",
       "        [-0.48417223,  0.13044941,  0.20019229, ...,  0.31398135,\n",
       "          0.47846095,  0.5410379 ],\n",
       "        [-0.02161192,  0.83645992, -1.72232249, ...,  0.5206349 ,\n",
       "          0.77590156,  0.7888651 ],\n",
       "        [-0.75032667,  0.00302417,  0.25706022, ...,  0.176417  ,\n",
       "          0.33254689,  0.6171529 ]],\n",
       "\n",
       "       [[-1.24590196,  0.39769251, -1.07953581, ...,  0.47228019,\n",
       "          0.58820564,  1.00786879],\n",
       "        [ 0.07658676,  0.44436754, -0.10775569, ...,  0.35164887,\n",
       "          0.56250846,  0.44569387],\n",
       "        [ 0.36775983,  0.66519474, -0.41527372, ...,  0.38366941,\n",
       "          0.72657422,  0.34829413],\n",
       "        ...,\n",
       "        [-1.5115764 ,  0.1148255 , -0.35660545, ...,  0.39950074,\n",
       "          0.41919517,  0.91540561],\n",
       "        [-1.49876049,  0.24537499, -0.7159967 , ...,  0.77842317,\n",
       "          0.69616121,  0.81456567],\n",
       "        [ 0.31043389,  0.56632729,  0.00928256, ..., -0.39917585,\n",
       "          0.51313828,  0.5753074 ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4faba2aa-0645-41f7-ba6e-c20ee61386ea' class='xr-section-summary-in' type='checkbox'  ><label for='section-4faba2aa-0645-41f7-ba6e-c20ee61386ea' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-630b466b-57a5-4d21-b9d9-e58c5d9646e8' class='xr-index-data-in' type='checkbox'/><label for='index-630b466b-57a5-4d21-b9d9-e58c5d9646e8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1127bef0-d108-4e6a-970e-6a099e5e752f' class='xr-index-data-in' type='checkbox'/><label for='index-1127bef0-d108-4e6a-970e-6a099e5e752f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f420fd8-3f83-4409-bcea-8c8b5956e1f1' class='xr-index-data-in' type='checkbox'/><label for='index-1f420fd8-3f83-4409-bcea-8c8b5956e1f1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f8af4105-d94c-488f-8c91-a6c16607bc4d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f8af4105-d94c-488f-8c91-a6c16607bc4d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.609401</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats433d053f-0208-4ad0-9c74-95e482a57069\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats433d053f-0208-4ad0-9c74-95e482a57069\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "}\n",
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       "\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    acceptance_rate  (chain, draw) float64 0.9282 0.9287 ... 0.8313 0.9974\n",
       "    step_size        (chain, draw) float64 0.804 0.804 0.804 ... 0.6157 0.6157\n",
       "    tree_depth       (chain, draw) int64 2 2 2 3 2 1 3 3 2 ... 2 2 3 3 2 2 3 3 3\n",
       "    n_steps          (chain, draw) int64 3 3 3 7 3 3 7 7 3 ... 7 7 7 7 3 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -32.69 -31.76 -25.78 ... -28.81 -30.5\n",
       "    lp               (chain, draw) float64 32.9 32.41 32.41 ... 31.39 31.47\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.561609\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/elkgjo5c\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-be78a061-a07f-4b47-8dfe-833918e09bbe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-be78a061-a07f-4b47-8dfe-833918e09bbe' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c3591f2e-4cc8-4693-b80b-6443d3ad2d63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3591f2e-4cc8-4693-b80b-6443d3ad2d63' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e7acbf23-abbe-4b8c-a408-5ece60bae5d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7acbf23-abbe-4b8c-a408-5ece60bae5d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8079c42-2ca0-4f21-a1b6-48ae32ea3fdd' class='xr-var-data-in' type='checkbox'><label for='data-a8079c42-2ca0-4f21-a1b6-48ae32ea3fdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-d1d946f9-8cf5-4ee7-9a2e-dd67ea9dbee7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1d946f9-8cf5-4ee7-9a2e-dd67ea9dbee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2745132-b861-4a5e-8330-d522484b55d1' class='xr-var-data-in' type='checkbox'><label for='data-d2745132-b861-4a5e-8330-d522484b55d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd54900d-8c9d-450f-ba21-27777d1c2d66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd54900d-8c9d-450f-ba21-27777d1c2d66' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9282 0.9287 ... 0.8313 0.9974</div><input id='attrs-96e79531-03b7-4fe5-ba43-e428235e3369' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96e79531-03b7-4fe5-ba43-e428235e3369' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cd3c538-4812-4ff7-8add-b2bf39f908e8' class='xr-var-data-in' type='checkbox'><label for='data-9cd3c538-4812-4ff7-8add-b2bf39f908e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.92819443, 0.92869654, 0.4925525 , ..., 1.        , 0.94688117,\n",
       "        0.9589021 ],\n",
       "       [0.91644703, 0.69699634, 0.93612351, ..., 0.9364036 , 0.99420983,\n",
       "        0.81455585],\n",
       "       [0.96430137, 0.97409497, 0.99509768, ..., 0.97246677, 1.        ,\n",
       "        0.89354286],\n",
       "       [0.99704312, 0.9763358 , 0.94775592, ..., 0.98887934, 0.83131928,\n",
       "        0.99740717]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.804 0.804 0.804 ... 0.6157 0.6157</div><input id='attrs-54d0c879-b870-470f-a57b-a064fe320a7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54d0c879-b870-470f-a57b-a064fe320a7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-201aa960-5480-4041-bbd2-bd5be9c8694a' class='xr-var-data-in' type='checkbox'><label for='data-201aa960-5480-4041-bbd2-bd5be9c8694a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.80399961, 0.80399961, 0.80399961, ..., 0.80399961, 0.80399961,\n",
       "        0.80399961],\n",
       "       [0.64934786, 0.64934786, 0.64934786, ..., 0.64934786, 0.64934786,\n",
       "        0.64934786],\n",
       "       [0.66481378, 0.66481378, 0.66481378, ..., 0.66481378, 0.66481378,\n",
       "        0.66481378],\n",
       "       [0.61567518, 0.61567518, 0.61567518, ..., 0.61567518, 0.61567518,\n",
       "        0.61567518]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 3 2 1 3 3 ... 2 3 3 2 2 3 3 3</div><input id='attrs-9946d586-5796-40a2-9ce2-2454ffa4ba58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9946d586-5796-40a2-9ce2-2454ffa4ba58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca32bb3-daa6-405b-9a9b-32b6830641b7' class='xr-var-data-in' type='checkbox'><label for='data-eca32bb3-daa6-405b-9a9b-32b6830641b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 2, ..., 2, 3, 2],\n",
       "       [3, 2, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 3 7 3 3 7 7 ... 7 7 7 3 7 7 7 7</div><input id='attrs-ac535249-f331-45f4-a878-2a679a0579ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac535249-f331-45f4-a878-2a679a0579ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edb3074e-a10c-4af5-b062-8352b1149093' class='xr-var-data-in' type='checkbox'><label for='data-edb3074e-a10c-4af5-b062-8352b1149093' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 3, ..., 3, 7, 3],\n",
       "       [7, 3, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-845e08d5-f301-48b2-a4ab-2d8fca6f3e3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-845e08d5-f301-48b2-a4ab-2d8fca6f3e3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8667c012-4e57-4277-ab21-1ff8893f491f' class='xr-var-data-in' type='checkbox'><label for='data-8667c012-4e57-4277-ab21-1ff8893f491f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.69 -31.76 ... -28.81 -30.5</div><input id='attrs-d73166c8-310a-4960-b600-2beda1191fe2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d73166c8-310a-4960-b600-2beda1191fe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e77433e1-d58d-4375-b66e-01185875a0bc' class='xr-var-data-in' type='checkbox'><label for='data-e77433e1-d58d-4375-b66e-01185875a0bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-32.69384409, -31.7566032 , -25.77610549, ..., -27.216783  ,\n",
       "        -27.93455972, -28.54197578],\n",
       "       [-29.5652766 , -27.55071653, -20.7193842 , ..., -27.78535194,\n",
       "        -27.40559337, -26.647824  ],\n",
       "       [-30.97451467, -30.30297338, -31.25023388, ..., -29.48641369,\n",
       "        -30.80102109, -29.39750872],\n",
       "       [-30.80138424, -31.71419964, -29.15596048, ..., -30.72257641,\n",
       "        -28.81132411, -30.50102031]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>32.9 32.41 32.41 ... 31.39 31.47</div><input id='attrs-87fe4849-fc38-4b19-8a98-a081493ad072' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87fe4849-fc38-4b19-8a98-a081493ad072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ead638c9-a63d-48a1-8378-268dc433c395' class='xr-var-data-in' type='checkbox'><label for='data-ead638c9-a63d-48a1-8378-268dc433c395' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[32.89908803, 32.41317044, 32.41317044, ..., 29.51150957,\n",
       "        30.58242828, 30.09593041],\n",
       "       [32.59550577, 28.12209856, 22.76444362, ..., 29.37521675,\n",
       "        31.31213235, 29.14147989],\n",
       "       [32.11102076, 31.88232523, 32.49396748, ..., 31.65768369,\n",
       "        31.93884953, 31.14506966],\n",
       "       [32.59843988, 32.06648696, 32.06495111, ..., 32.49458917,\n",
       "        31.38999073, 31.46505698]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ca0a588c-1e90-4643-bc0b-0695f8de1db1' class='xr-section-summary-in' type='checkbox'  ><label for='section-ca0a588c-1e90-4643-bc0b-0695f8de1db1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a29057f-ca20-473a-b149-f47495527268' class='xr-index-data-in' type='checkbox'/><label for='index-3a29057f-ca20-473a-b149-f47495527268' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3393a66d-615c-4e55-bb5b-4aaecb334cd7' class='xr-index-data-in' type='checkbox'/><label for='index-3393a66d-615c-4e55-bb5b-4aaecb334cd7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d78ba059-8ea3-4009-abc1-d8d0e8d4671e' class='xr-section-summary-in' type='checkbox'  ><label for='section-d78ba059-8ea3-4009-abc1-d8d0e8d4671e' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.561609</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/elkgjo5c</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior72466261-5582-4cef-bbd9-6100ec154ed8\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_prior72466261-5582-4cef-bbd9-6100ec154ed8\" class = \"xr-section-summary\">prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (chain: 4, draw: 1000, mu_dim_0: 27,\n",
       "                           log_lik_dim_0: 27, blooms_std_hat_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain                 (chain) int64 0 1 2 3\n",
       "  * draw                  (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * mu_dim_0              (mu_dim_0) int64 0 1 2 3 4 5 6 ... 21 22 23 24 25 26\n",
       "  * log_lik_dim_0         (log_lik_dim_0) int64 0 1 2 3 4 5 ... 22 23 24 25 26\n",
       "  * blooms_std_hat_dim_0  (blooms_std_hat_dim_0) int64 0 1 2 3 4 ... 23 24 25 26\n",
       "Data variables:\n",
       "    alpha                 (chain, draw) float64 0.3615 0.3282 ... 0.3922 0.34\n",
       "    beta_w                (chain, draw) float64 0.2235 0.2231 ... 0.2044 0.2055\n",
       "    beta_s                (chain, draw) float64 -0.08126 -0.07431 ... -0.04058\n",
       "    sigma                 (chain, draw) float64 0.1818 0.1567 ... 0.1719 0.1567\n",
       "    mu                    (chain, draw, mu_dim_0) float64 0.2193 ... 0.505\n",
       "    log_lik               (chain, draw, log_lik_dim_0) float64 0.05831 ... 0....\n",
       "    blooms_std_hat        (chain, draw, blooms_std_hat_dim_0) float64 0.1343 ...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.701908\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/elkgjo5c\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fd6b3bd3-feb7-4983-8490-b27426420561' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd6b3bd3-feb7-4983-8490-b27426420561' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 27</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 27</li><li><span class='xr-has-index'>blooms_std_hat_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8828172b-9ee9-4901-82d6-5db26fd5cfaa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8828172b-9ee9-4901-82d6-5db26fd5cfaa' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-656ac6e4-e20e-4d08-82e0-a38a44003b6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-656ac6e4-e20e-4d08-82e0-a38a44003b6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26ad7207-41f5-4af6-9afb-9967ff1a5436' class='xr-var-data-in' type='checkbox'><label for='data-26ad7207-41f5-4af6-9afb-9967ff1a5436' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-dfb54531-0d6b-4fa1-ad9b-0a386fe9e2e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfb54531-0d6b-4fa1-ad9b-0a386fe9e2e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62e15aef-cc96-472c-b2f2-044f11973470' class='xr-var-data-in' type='checkbox'><label for='data-62e15aef-cc96-472c-b2f2-044f11973470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-5757bd30-32b7-4e8e-b21f-661b95e4510a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5757bd30-32b7-4e8e-b21f-661b95e4510a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa3fa8a4-70fe-4610-a562-fbc4b6224945' class='xr-var-data-in' type='checkbox'><label for='data-aa3fa8a4-70fe-4610-a562-fbc4b6224945' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-193dd8f5-4d19-4628-ad00-e24fb0c28e9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-193dd8f5-4d19-4628-ad00-e24fb0c28e9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-692dde14-cb3e-4f82-b898-3c0d70cf67da' class='xr-var-data-in' type='checkbox'><label for='data-692dde14-cb3e-4f82-b898-3c0d70cf67da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>blooms_std_hat_dim_0</span></div><div class='xr-var-dims'>(blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-8909d007-a5bf-47a1-83cb-656e285d8ab4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8909d007-a5bf-47a1-83cb-656e285d8ab4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-423a31a6-5e69-4c03-8c97-679a1fcc1de2' class='xr-var-data-in' type='checkbox'><label for='data-423a31a6-5e69-4c03-8c97-679a1fcc1de2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-147aaf8f-cad0-4f25-9dfa-c3da7e1e3cb0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-147aaf8f-cad0-4f25-9dfa-c3da7e1e3cb0' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3615 0.3282 ... 0.3922 0.34</div><input id='attrs-321cc144-d13a-4bd1-96a2-0af6884b901f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-321cc144-d13a-4bd1-96a2-0af6884b901f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03403dc8-89ad-4b8c-9e2d-4b30683908bc' class='xr-var-data-in' type='checkbox'><label for='data-03403dc8-89ad-4b8c-9e2d-4b30683908bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.36151912, 0.32815769, 0.32815769, ..., 0.398854  , 0.40135532,\n",
       "        0.34068593],\n",
       "       [0.38940917, 0.36037667, 0.22408745, ..., 0.3180109 , 0.40176888,\n",
       "        0.28706318],\n",
       "       [0.36522773, 0.36000492, 0.37166953, ..., 0.39080708, 0.33868423,\n",
       "        0.3814309 ],\n",
       "       [0.35703264, 0.36576697, 0.35507678, ..., 0.36614084, 0.39216189,\n",
       "        0.34001226]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_w</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2235 0.2231 ... 0.2044 0.2055</div><input id='attrs-dd72e866-fcb0-4276-a524-a5f894898ee9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd72e866-fcb0-4276-a524-a5f894898ee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eea96be7-8068-4f56-bc5c-fe4debb2c834' class='xr-var-data-in' type='checkbox'><label for='data-eea96be7-8068-4f56-bc5c-fe4debb2c834' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2234682 , 0.22313142, 0.22313142, ..., 0.24644808, 0.23838356,\n",
       "        0.27217199],\n",
       "       [0.18162366, 0.258444  , 0.14405701, ..., 0.27981642, 0.15127777,\n",
       "        0.24238516],\n",
       "       [0.16660002, 0.24548443, 0.18937575, ..., 0.1784585 , 0.2471408 ,\n",
       "        0.14368408],\n",
       "       [0.19808622, 0.22830251, 0.25519313, ..., 0.16415032, 0.20440446,\n",
       "        0.20553722]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.08126 -0.07431 ... -0.04058</div><input id='attrs-f20cc6a0-0885-4363-b905-e07d26aba203' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f20cc6a0-0885-4363-b905-e07d26aba203' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4985072c-6400-4094-86e1-e4001ed5f131' class='xr-var-data-in' type='checkbox'><label for='data-4985072c-6400-4094-86e1-e4001ed5f131' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.08125679, -0.07430747, -0.07430747, ..., -0.01521865,\n",
       "        -0.15071864, -0.18597174],\n",
       "       [-0.13930466, -0.1461497 , -0.21153582, ..., -0.05536522,\n",
       "        -0.09621488, -0.06535812],\n",
       "       [-0.06508399, -0.16381473, -0.06090548, ..., -0.10267189,\n",
       "        -0.11255645, -0.11444698],\n",
       "       [-0.13711154, -0.084861  , -0.06737803, ..., -0.14948422,\n",
       "        -0.18413257, -0.04057832]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1818 0.1567 ... 0.1719 0.1567</div><input id='attrs-9fb04cbf-70df-46f4-8754-117d771b3940' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fb04cbf-70df-46f4-8754-117d771b3940' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a018b42-b465-4f02-b39d-5d8d6c211cbd' class='xr-var-data-in' type='checkbox'><label for='data-4a018b42-b465-4f02-b39d-5d8d6c211cbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18179702, 0.15674123, 0.15674123, ..., 0.19407449, 0.21766417,\n",
       "        0.1953823 ],\n",
       "       [0.17956849, 0.26526528, 0.19453234, ..., 0.21582145, 0.15505213,\n",
       "        0.22309311],\n",
       "       [0.15463377, 0.18767444, 0.16014579, ..., 0.20987668, 0.1392491 ,\n",
       "        0.21168626],\n",
       "       [0.13776296, 0.2037366 , 0.17394901, ..., 0.15948283, 0.17187688,\n",
       "        0.15665806]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2193 0.1381 ... 0.5455 0.505</div><input id='attrs-d9aab9fe-3cb0-487d-af6b-377cfda22123' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9aab9fe-3cb0-487d-af6b-377cfda22123' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-001d07c2-87d8-4bc8-9b24-ddab0b019735' class='xr-var-data-in' type='checkbox'><label for='data-001d07c2-87d8-4bc8-9b24-ddab0b019735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.21930771,  0.13805092,  0.05679413, ...,  0.66624411,\n",
       "          0.58498732,  0.50373053],\n",
       "        [ 0.17933374,  0.10502627,  0.0307188 , ...,  0.62559657,\n",
       "          0.55128911,  0.47698164],\n",
       "        [ 0.17933374,  0.10502627,  0.0307188 , ...,  0.62559657,\n",
       "          0.55128911,  0.47698164],\n",
       "        ...,\n",
       "        [ 0.16762457,  0.15240592,  0.13718726, ...,  0.66052074,\n",
       "          0.64530208,  0.63008343],\n",
       "        [ 0.31369039,  0.16297176,  0.01225312, ...,  0.79045752,\n",
       "          0.63973888,  0.48902024],\n",
       "        [ 0.25448567,  0.06851393, -0.11745781, ...,  0.79882966,\n",
       "          0.61285792,  0.42688618]],\n",
       "\n",
       "       [[ 0.34709017,  0.20778551,  0.06848084, ...,  0.71033749,\n",
       "          0.57103283,  0.43172817],\n",
       "        [ 0.24808237,  0.10193267, -0.04421703, ...,  0.76497037,\n",
       "          0.61882067,  0.47267097],\n",
       "        [ 0.29156627,  0.08003045, -0.13150537, ...,  0.57968028,\n",
       "          0.36814446,  0.15660864],\n",
       "...\n",
       "        [ 0.31502047,  0.21234858,  0.10967668, ...,  0.67193747,\n",
       "          0.56926558,  0.46659368],\n",
       "        [ 0.20409988,  0.09154342, -0.02101303, ...,  0.69838148,\n",
       "          0.58582503,  0.47326857],\n",
       "        [ 0.35219379,  0.23774682,  0.12329984, ...,  0.63956196,\n",
       "          0.52511499,  0.41066801]],\n",
       "\n",
       "       [[ 0.29605796,  0.15894642,  0.02183489, ...,  0.6922304 ,\n",
       "          0.55511887,  0.41800733],\n",
       "        [ 0.22232546,  0.13746446,  0.05260346, ...,  0.67893049,\n",
       "          0.59406949,  0.50920849],\n",
       "        [ 0.16726167,  0.09988364,  0.03250561, ...,  0.67764794,\n",
       "          0.61026991,  0.54289188],\n",
       "        ...,\n",
       "        [ 0.35147474,  0.20199052,  0.05250631, ...,  0.67977538,\n",
       "          0.53029116,  0.38080695],\n",
       "        [ 0.37189   ,  0.18775743,  0.00362486, ...,  0.78069892,\n",
       "          0.59656635,  0.41243378],\n",
       "        [ 0.17505336,  0.13447504,  0.09389672, ...,  0.58612781,\n",
       "          0.54554949,  0.50497117]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05831 0.4976 ... 0.5131 0.5753</div><input id='attrs-87e25168-037e-403a-896a-07686a4119e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87e25168-037e-403a-896a-07686a4119e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3075299-9ae6-4106-8bbd-f95860ece484' class='xr-var-data-in' type='checkbox'><label for='data-b3075299-9ae6-4106-8bbd-f95860ece484' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.05830657,  0.49760574, -0.16138181, ...,  0.3185704 ,\n",
       "          0.62098075,  0.52397977],\n",
       "        [ 0.27969359,  0.70972927, -0.619584  , ..., -0.01892029,\n",
       "          0.54599227,  0.71053968],\n",
       "        [ 0.27969359,  0.70972927, -0.619584  , ..., -0.01892029,\n",
       "          0.54599227,  0.71053968],\n",
       "        ...,\n",
       "        [ 0.34757501,  0.4122298 ,  0.3376438 , ...,  0.28333732,\n",
       "          0.69475471, -0.16263707],\n",
       "        [-0.43261763,  0.32556486, -0.31115729, ...,  0.57781984,\n",
       "          0.57983146,  0.46170485],\n",
       "        [-0.13439604,  0.65237525, -1.6462248 , ...,  0.68944129,\n",
       "          0.63711356,  0.6746098 ]],\n",
       "\n",
       "       [[-1.06981528,  0.12877598, -0.0841304 , ...,  0.52943141,\n",
       "          0.58098762,  0.74321033],\n",
       "        [-0.02923529,  0.33425582, -0.46857385, ...,  0.3659171 ,\n",
       "          0.3726851 ,  0.33627968],\n",
       "        [-0.40499163,  0.6335937 , -1.82271394, ..., -0.19100204,\n",
       "         -0.6454212 ,  0.10441687],\n",
       "...\n",
       "        [-0.48417223,  0.13044941,  0.20019229, ...,  0.31398135,\n",
       "          0.47846095,  0.5410379 ],\n",
       "        [-0.02161192,  0.83645992, -1.72232249, ...,  0.5206349 ,\n",
       "          0.77590156,  0.7888651 ],\n",
       "        [-0.75032667,  0.00302417,  0.25706022, ...,  0.176417  ,\n",
       "          0.33254689,  0.6171529 ]],\n",
       "\n",
       "       [[-1.24590196,  0.39769251, -1.07953581, ...,  0.47228019,\n",
       "          0.58820564,  1.00786879],\n",
       "        [ 0.07658676,  0.44436754, -0.10775569, ...,  0.35164887,\n",
       "          0.56250846,  0.44569387],\n",
       "        [ 0.36775983,  0.66519474, -0.41527372, ...,  0.38366941,\n",
       "          0.72657422,  0.34829413],\n",
       "        ...,\n",
       "        [-1.5115764 ,  0.1148255 , -0.35660545, ...,  0.39950074,\n",
       "          0.41919517,  0.91540561],\n",
       "        [-1.49876049,  0.24537499, -0.7159967 , ...,  0.77842317,\n",
       "          0.69616121,  0.81456567],\n",
       "        [ 0.31043389,  0.56632729,  0.00928256, ..., -0.39917585,\n",
       "          0.51313828,  0.5753074 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blooms_std_hat</span></div><div class='xr-var-dims'>(chain, draw, blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1343 0.5943 ... 0.3915 0.7434</div><input id='attrs-870d1d95-5fa6-4ad0-850d-b0182b19dac9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-870d1d95-5fa6-4ad0-850d-b0182b19dac9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b6b1fc8-af55-4f22-b03c-2d2349058345' class='xr-var-data-in' type='checkbox'><label for='data-3b6b1fc8-af55-4f22-b03c-2d2349058345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.13431731,  0.59425404,  0.01385853, ...,  0.66095709,\n",
       "          0.60488816,  0.20570972],\n",
       "        [ 0.17186603, -0.27925228,  0.0762902 , ...,  0.41903908,\n",
       "          0.67828193,  0.7832615 ],\n",
       "        [ 0.32582762,  0.13745404, -0.12311134, ...,  0.60691657,\n",
       "          0.74174309,  0.55835433],\n",
       "        ...,\n",
       "        [ 0.4789924 ,  0.12740402,  0.30143615, ...,  0.62751355,\n",
       "          0.76821062,  0.6914642 ],\n",
       "        [ 0.19186969,  0.26657567, -0.2792809 , ...,  1.06077298,\n",
       "          0.3942048 ,  0.62214776],\n",
       "        [ 0.28479492, -0.15423803,  0.17273948, ...,  0.42106936,\n",
       "          0.40280386,  0.98798013]],\n",
       "\n",
       "       [[ 0.32413327,  0.37987283,  0.03563597, ...,  0.38697192,\n",
       "          0.58961431,  0.40477897],\n",
       "        [ 0.2183097 ,  0.32593148, -0.50498804, ...,  0.95798906,\n",
       "          0.96302477,  0.56147575],\n",
       "        [ 0.28305719,  0.16280085, -0.34164725, ...,  0.80950041,\n",
       "          0.2899334 ,  0.44065836],\n",
       "...\n",
       "        [ 0.21929111,  0.63570024, -0.23934634, ...,  0.37549196,\n",
       "          0.72854746,  0.40837225],\n",
       "        [ 0.31968915, -0.20197696, -0.02666252, ...,  0.86668474,\n",
       "          0.76436461,  0.51641614],\n",
       "        [ 0.65702522,  0.09785624,  0.11138776, ...,  0.633106  ,\n",
       "          0.70392453,  0.25061156]],\n",
       "\n",
       "       [[ 0.16423277, -0.04556628, -0.02973752, ...,  0.67547891,\n",
       "          0.53918786,  0.63141729],\n",
       "        [ 0.01233017,  0.14773807, -0.14854822, ...,  0.8450887 ,\n",
       "          0.63403973,  0.30292474],\n",
       "        [ 0.15729091,  0.03893416,  0.26528013, ...,  0.52734266,\n",
       "          0.67691094,  0.47492956],\n",
       "        ...,\n",
       "        [ 0.6594963 ,  0.43112621,  0.00899109, ...,  0.53530003,\n",
       "          0.54954733,  0.33535696],\n",
       "        [ 0.53931735,  0.1930282 , -0.17342449, ...,  0.56710126,\n",
       "          0.50395754,  0.6162502 ],\n",
       "        [ 0.12728881,  0.39542413,  0.24333331, ...,  0.63115714,\n",
       "          0.39148701,  0.74344369]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c61c1d6a-5aea-4216-a57a-a9a8bc22480e' class='xr-section-summary-in' type='checkbox'  ><label for='section-c61c1d6a-5aea-4216-a57a-a9a8bc22480e' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1587716-5f0a-4a8f-9d4e-f4e8307b9048' class='xr-index-data-in' type='checkbox'/><label for='index-a1587716-5f0a-4a8f-9d4e-f4e8307b9048' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-068da219-f367-41f7-815e-1eb012baa4ed' class='xr-index-data-in' type='checkbox'/><label for='index-068da219-f367-41f7-815e-1eb012baa4ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca129c70-9f16-40e8-9b9a-774e4c4c9def' class='xr-index-data-in' type='checkbox'/><label for='index-ca129c70-9f16-40e8-9b9a-774e4c4c9def' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4c51896-b173-43c2-a49f-e9a528e0cf06' class='xr-index-data-in' type='checkbox'/><label for='index-d4c51896-b173-43c2-a49f-e9a528e0cf06' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>blooms_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3f3a5fa0-c3eb-49ea-a31c-994a89fbfeff' class='xr-index-data-in' type='checkbox'/><label for='index-3f3a5fa0-c3eb-49ea-a31c-994a89fbfeff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;blooms_std_hat_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80df1663-8b67-40df-83e4-03ef7de33152' class='xr-section-summary-in' type='checkbox'  ><label for='section-80df1663-8b67-40df-83e4-03ef7de33152' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.701908</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/elkgjo5c</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats_prior462fbe51-d78a-4d1e-8286-6f85c0152584\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats_prior462fbe51-d78a-4d1e-8286-6f85c0152584\" class = \"xr-section-summary\">sample_stats_prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "body[data-theme=dark],\n",
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 32.9 32.41 32.41 ... 31.39 31.47\n",
       "    acceptance_rate  (chain, draw) float64 0.9282 0.9287 ... 0.8313 0.9974\n",
       "    step_size        (chain, draw) float64 0.804 0.804 0.804 ... 0.6157 0.6157\n",
       "    tree_depth       (chain, draw) int64 2 2 2 3 2 1 3 3 2 ... 2 2 3 3 2 2 3 3 3\n",
       "    n_steps          (chain, draw) int64 3 3 3 7 3 3 7 7 3 ... 7 7 7 7 3 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -32.69 -31.76 -25.78 ... -28.81 -30.5\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.750637\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/elkgjo5c\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c35b53c1-db6c-4508-9fad-e14f95e8c42c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c35b53c1-db6c-4508-9fad-e14f95e8c42c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bbc61e4a-ce58-4f0e-a4d8-55dde2a78124' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbc61e4a-ce58-4f0e-a4d8-55dde2a78124' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-2f2f6e7f-4c23-4427-a2c2-2047dd00bff8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f2f6e7f-4c23-4427-a2c2-2047dd00bff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afbcfa38-716c-4255-86be-6acd269d4a93' class='xr-var-data-in' type='checkbox'><label for='data-afbcfa38-716c-4255-86be-6acd269d4a93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ee10b25d-0ff2-4e99-ab1a-997b5f36d2c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee10b25d-0ff2-4e99-ab1a-997b5f36d2c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9179806-2686-49c1-a2db-fe4f51985974' class='xr-var-data-in' type='checkbox'><label for='data-a9179806-2686-49c1-a2db-fe4f51985974' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47fd5b4e-b51c-4f07-a07e-d6f3a70c3e21' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47fd5b4e-b51c-4f07-a07e-d6f3a70c3e21' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>32.9 32.41 32.41 ... 31.39 31.47</div><input id='attrs-ba1b60e4-2f8f-4807-a158-8e9ab054b838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba1b60e4-2f8f-4807-a158-8e9ab054b838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07132809-548d-4995-8ac9-97eca1ea6bf7' class='xr-var-data-in' type='checkbox'><label for='data-07132809-548d-4995-8ac9-97eca1ea6bf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[32.89908803, 32.41317044, 32.41317044, ..., 29.51150957,\n",
       "        30.58242828, 30.09593041],\n",
       "       [32.59550577, 28.12209856, 22.76444362, ..., 29.37521675,\n",
       "        31.31213235, 29.14147989],\n",
       "       [32.11102076, 31.88232523, 32.49396748, ..., 31.65768369,\n",
       "        31.93884953, 31.14506966],\n",
       "       [32.59843988, 32.06648696, 32.06495111, ..., 32.49458917,\n",
       "        31.38999073, 31.46505698]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9282 0.9287 ... 0.8313 0.9974</div><input id='attrs-5f7c0fb5-0a16-4be8-ba69-a758ff6f9667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f7c0fb5-0a16-4be8-ba69-a758ff6f9667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c35ec810-51b2-48ad-b40a-0316b6a74a7f' class='xr-var-data-in' type='checkbox'><label for='data-c35ec810-51b2-48ad-b40a-0316b6a74a7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.92819443, 0.92869654, 0.4925525 , ..., 1.        , 0.94688117,\n",
       "        0.9589021 ],\n",
       "       [0.91644703, 0.69699634, 0.93612351, ..., 0.9364036 , 0.99420983,\n",
       "        0.81455585],\n",
       "       [0.96430137, 0.97409497, 0.99509768, ..., 0.97246677, 1.        ,\n",
       "        0.89354286],\n",
       "       [0.99704312, 0.9763358 , 0.94775592, ..., 0.98887934, 0.83131928,\n",
       "        0.99740717]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.804 0.804 0.804 ... 0.6157 0.6157</div><input id='attrs-fe8104d7-c322-4429-9c29-f7540d2432b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe8104d7-c322-4429-9c29-f7540d2432b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8cb6eb-9b9b-4121-b4d4-46eab83ad890' class='xr-var-data-in' type='checkbox'><label for='data-6d8cb6eb-9b9b-4121-b4d4-46eab83ad890' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.80399961, 0.80399961, 0.80399961, ..., 0.80399961, 0.80399961,\n",
       "        0.80399961],\n",
       "       [0.64934786, 0.64934786, 0.64934786, ..., 0.64934786, 0.64934786,\n",
       "        0.64934786],\n",
       "       [0.66481378, 0.66481378, 0.66481378, ..., 0.66481378, 0.66481378,\n",
       "        0.66481378],\n",
       "       [0.61567518, 0.61567518, 0.61567518, ..., 0.61567518, 0.61567518,\n",
       "        0.61567518]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 3 2 1 3 3 ... 2 3 3 2 2 3 3 3</div><input id='attrs-2a6e965c-5cf4-4e2c-8f25-7ea0f054a7c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a6e965c-5cf4-4e2c-8f25-7ea0f054a7c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-291f75c3-dfe1-419d-9e71-6c038cf122cc' class='xr-var-data-in' type='checkbox'><label for='data-291f75c3-dfe1-419d-9e71-6c038cf122cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 2, ..., 2, 3, 2],\n",
       "       [3, 2, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3 3 3 7 3 3 7 7 ... 7 7 7 3 7 7 7 7</div><input id='attrs-562d1aa6-44ff-4fc1-aa2e-78f320a68460' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-562d1aa6-44ff-4fc1-aa2e-78f320a68460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3716e24-d411-4c0b-9aa7-11895f2340f3' class='xr-var-data-in' type='checkbox'><label for='data-f3716e24-d411-4c0b-9aa7-11895f2340f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 3, ..., 3, 7, 3],\n",
       "       [7, 3, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-36d5d57b-7887-45a6-94bf-b54b20c63caa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36d5d57b-7887-45a6-94bf-b54b20c63caa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0e7ff45-3c7b-4e1c-a4ef-0fae39c576ee' class='xr-var-data-in' type='checkbox'><label for='data-a0e7ff45-3c7b-4e1c-a4ef-0fae39c576ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.69 -31.76 ... -28.81 -30.5</div><input id='attrs-863d2e16-da73-4888-bd7b-22958fe6fc11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-863d2e16-da73-4888-bd7b-22958fe6fc11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30ce8a62-35a8-4574-a6b7-232dbbbf794d' class='xr-var-data-in' type='checkbox'><label for='data-30ce8a62-35a8-4574-a6b7-232dbbbf794d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-32.69384409, -31.7566032 , -25.77610549, ..., -27.216783  ,\n",
       "        -27.93455972, -28.54197578],\n",
       "       [-29.5652766 , -27.55071653, -20.7193842 , ..., -27.78535194,\n",
       "        -27.40559337, -26.647824  ],\n",
       "       [-30.97451467, -30.30297338, -31.25023388, ..., -29.48641369,\n",
       "        -30.80102109, -29.39750872],\n",
       "       [-30.80138424, -31.71419964, -29.15596048, ..., -30.72257641,\n",
       "        -28.81132411, -30.50102031]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-825bd73f-2ce7-46e6-a3c9-19078a19975d' class='xr-section-summary-in' type='checkbox'  ><label for='section-825bd73f-2ce7-46e6-a3c9-19078a19975d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-966d1360-c06a-46ad-8114-bfde28ce8bd4' class='xr-index-data-in' type='checkbox'/><label for='index-966d1360-c06a-46ad-8114-bfde28ce8bd4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00f893fa-6d4f-4e11-8223-d1e4c44f6c0f' class='xr-index-data-in' type='checkbox'/><label for='index-00f893fa-6d4f-4e11-8223-d1e4c44f6c0f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b62b122-f1ad-4181-a9fc-5aede2893286' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b62b122-f1ad-4181-a9fc-5aede2893286' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.750637</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/elkgjo5c</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
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       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_dataf7ce0748-bcb1-4f77-bd61-3ccc1974623c\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "<defs>\n",
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       "\n",
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       ".xr-text-repr-fallback {\n",
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       "\n",
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       "  margin-bottom: 4px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
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       "}\n",
       "\n",
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       ".xr-var-item label,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (blooms_std_dim_0: 27, water_cent_dim_0: 27,\n",
       "                       shade_cent_dim_0: 27)\n",
       "Coordinates:\n",
       "  * blooms_std_dim_0  (blooms_std_dim_0) int64 0 1 2 3 4 5 ... 21 22 23 24 25 26\n",
       "  * water_cent_dim_0  (water_cent_dim_0) int64 0 1 2 3 4 5 ... 21 22 23 24 25 26\n",
       "  * shade_cent_dim_0  (shade_cent_dim_0) int64 0 1 2 3 4 5 ... 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    blooms_std        (blooms_std_dim_0) float64 0.0 0.0 0.307 ... 0.6894 0.3721\n",
       "    water_cent        (water_cent_dim_0) float64 -1.0 -1.0 -1.0 ... 1.0 1.0 1.0\n",
       "    shade_cent        (shade_cent_dim_0) float64 -1.0 0.0 1.0 ... -1.0 0.0 1.0\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:33.478728\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3f18ab54-bc91-476c-86d7-7f0373b2846f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f18ab54-bc91-476c-86d7-7f0373b2846f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>blooms_std_dim_0</span>: 27</li><li><span class='xr-has-index'>water_cent_dim_0</span>: 27</li><li><span class='xr-has-index'>shade_cent_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-475553d6-0ec0-4abf-8b4f-2f62db9e2a7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-475553d6-0ec0-4abf-8b4f-2f62db9e2a7e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>blooms_std_dim_0</span></div><div class='xr-var-dims'>(blooms_std_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-de1ce79d-8c1f-45eb-8fb0-2b5c4bbd1138' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de1ce79d-8c1f-45eb-8fb0-2b5c4bbd1138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-587744d6-3f3a-44ab-b15b-d5a1a46b387a' class='xr-var-data-in' type='checkbox'><label for='data-587744d6-3f3a-44ab-b15b-d5a1a46b387a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>water_cent_dim_0</span></div><div class='xr-var-dims'>(water_cent_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-ad47bc85-c131-4dd6-b54b-74af6539ec8c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad47bc85-c131-4dd6-b54b-74af6539ec8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a6ac8f7-ff8f-4eef-ba1b-a6b428a6c37e' class='xr-var-data-in' type='checkbox'><label for='data-9a6ac8f7-ff8f-4eef-ba1b-a6b428a6c37e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>shade_cent_dim_0</span></div><div class='xr-var-dims'>(shade_cent_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-f6bc228b-cfe4-4f82-bcda-bf03609f4cd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6bc228b-cfe4-4f82-bcda-bf03609f4cd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d53e81f6-61c7-414a-9b5e-0d4cb1b1c086' class='xr-var-data-in' type='checkbox'><label for='data-d53e81f6-61c7-414a-9b5e-0d4cb1b1c086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61d67090-adee-4f61-9b7f-75471ddcffd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61d67090-adee-4f61-9b7f-75471ddcffd8' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blooms_std</span></div><div class='xr-var-dims'>(blooms_std_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.307 ... 0.6894 0.3721</div><input id='attrs-4bff398a-0374-48fd-8611-b615bf4b2a0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bff398a-0374-48fd-8611-b615bf4b2a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c34bc4-5bf0-4831-8e84-b4ebee395dbb' class='xr-var-data-in' type='checkbox'><label for='data-18c34bc4-5bf0-4831-8e84-b4ebee395dbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.        , 0.        , 0.3070287 , 0.50729967, 0.16357905,\n",
       "       0.21221589, 0.62204833, 0.23162639, 0.37314052, 0.22147874,\n",
       "       0.23765415, 0.0549411 , 0.5893104 , 0.34560084, 0.18105403,\n",
       "       1.        , 0.54506996, 0.37308522, 0.02770558, 0.13186418,\n",
       "       0.29516673, 0.68019687, 0.3758226 , 0.25067743, 0.8420063 ,\n",
       "       0.68940441, 0.37214511])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_cent</span></div><div class='xr-var-dims'>(water_cent_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -1.0 -1.0 0.0 ... 1.0 1.0 1.0</div><input id='attrs-408314cf-6f6d-409b-9d50-af1304a1a4e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-408314cf-6f6d-409b-9d50-af1304a1a4e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71890c72-55b2-4da6-a394-568edbc4274d' class='xr-var-data-in' type='checkbox'><label for='data-71890c72-55b2-4da6-a394-568edbc4274d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1., -1., -1.,  0.,  0.,  0.,  1.,  1.,  1., -1., -1., -1.,  0.,\n",
       "        0.,  0.,  1.,  1.,  1., -1., -1., -1.,  0.,  0.,  0.,  1.,  1.,\n",
       "        1.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shade_cent</span></div><div class='xr-var-dims'>(shade_cent_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 0.0 1.0 -1.0 ... -1.0 0.0 1.0</div><input id='attrs-47b7061b-df0a-41db-9e10-cb9b18e83659' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b7061b-df0a-41db-9e10-cb9b18e83659' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b261707-87cd-4e74-8b3c-52bb7b37462a' class='xr-var-data-in' type='checkbox'><label for='data-3b261707-87cd-4e74-8b3c-52bb7b37462a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.,  0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,\n",
       "        0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,  0.,\n",
       "        1.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e67c72c3-d2eb-410a-b380-71931ad64fe1' class='xr-section-summary-in' type='checkbox'  ><label for='section-e67c72c3-d2eb-410a-b380-71931ad64fe1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>blooms_std_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-11883cf1-e089-4afb-8e44-acd53dca3d46' class='xr-index-data-in' type='checkbox'/><label for='index-11883cf1-e089-4afb-8e44-acd53dca3d46' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;blooms_std_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>water_cent_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64c3cdfa-7642-4e3c-a1c2-17c02f743b83' class='xr-index-data-in' type='checkbox'/><label for='index-64c3cdfa-7642-4e3c-a1c2-17c02f743b83' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;water_cent_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>shade_cent_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56db5fc5-2443-4293-8dcf-e08f633af7a9' class='xr-index-data-in' type='checkbox'/><label for='index-56db5fc5-2443-4293-8dcf-e08f633af7a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;shade_cent_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66d1c3d0-b4d1-44c7-b3a6-adb5a79661f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66d1c3d0-b4d1-44c7-b3a6-adb5a79661f4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:33.478728</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> sample_stats_prior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stan_blooms = az.from_pystan(\n",
    "    prior_model=posteriori,\n",
    "    prior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples,\n",
    "    posterior_predictive=\"blooms_std_hat\",\n",
    "    observed_data=['blooms_std', 'water_cent', 'shade_cent'],\n",
    ")\n",
    "\n",
    "stan_blooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9868b64e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
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       "      <td>0.359</td>\n",
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       "      <td>0.429</td>\n",
       "      <td>0.001</td>\n",
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       "         mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha   0.359  0.034   0.298    0.429      0.001      0.0    3857.0    2579.0   \n",
       "beta_w  0.203  0.041   0.126    0.279      0.001      0.0    4353.0    3032.0   \n",
       "beta_s -0.112  0.041  -0.188   -0.036      0.001      0.0    4151.0    2524.0   \n",
       "\n",
       "        r_hat  \n",
       "alpha     1.0  \n",
       "beta_w    1.0  \n",
       "beta_s    1.0  "
      ]
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   "source": [
    "az.summary(stan_blooms, var_names=['alpha', 'beta_w', 'beta_s'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27706f0f",
   "metadata": {},
   "source": [
    "### R Code 8.24"
   ]
  },
  {
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   "id": "672f8341",
   "metadata": {},
   "source": [
    "$$ B_i \\sim Normal(\\mu_i, \\sigma) $$\n",
    "\n",
    "$$ \\mu_i = \\alpha + \\beta_W W_i + \\beta_S S_i + \\beta_{WS} S_i W_i $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cdb71ac3",
   "metadata": {
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBuilding:\u001b[0m found in cache, done.\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  50% (4000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  75% (6000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m 100% (8000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[32mSampling:\u001b[0m 100% (8000/8000), done.\n",
      "\u001b[36mMessages received during sampling:\u001b[0m\n",
      "  Gradient evaluation took 1.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] blooms_std;\n",
    "        vector[N] water_cent;\n",
    "        vector[N] shade_cent;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta_w;\n",
    "        real beta_s;\n",
    "        real beta_ws;\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta_w * water_cent + beta_s * shade_cent + beta_ws * water_cent .* shade_cent;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Priori\n",
    "        \n",
    "        alpha ~ normal(0.5, 0.25);\n",
    "        beta_w ~ normal(0, 0.25);\n",
    "        beta_s ~ normal(0, 0.25);\n",
    "        beta_ws ~ normal(0, 0.25);\n",
    "        sigma ~ exponential(1);\n",
    "        \n",
    "        blooms_std ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;\n",
    "        vector[N] blooms_std_hat;\n",
    "        \n",
    "        for(i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
    "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(df),\n",
    "    'blooms_std': df.blooms_std.values,   \n",
    "    'water_cent': df.water_cent.values,\n",
    "    'shade_cent': df.shade_cent.values,\n",
    "}\n",
    "\n",
    "posteriori = stan.build(model, data=data)\n",
    "samples = posteriori.sample(num_chains=4, num_samples=1000)"
   ]
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   "execution_count": 53,
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       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (chain: 4, draw: 1000, mu_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 0 1 2 3\n",
       "  * draw      (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * mu_dim_0  (mu_dim_0) int64 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    alpha     (chain, draw) float64 0.3674 0.3462 0.354 ... 0.3529 0.3582 0.3258\n",
       "    beta_w    (chain, draw) float64 0.2106 0.2189 0.1748 ... 0.2551 0.2051\n",
       "    beta_s    (chain, draw) float64 -0.1585 -0.1735 ... -0.1014 -0.06658\n",
       "    beta_ws   (chain, draw) float64 -0.2024 -0.1429 -0.1649 ... -0.08582 -0.1958\n",
       "    sigma     (chain, draw) float64 0.1466 0.1574 0.1383 ... 0.1805 0.1231\n",
       "    mu        (chain, draw, mu_dim_0) float64 0.1129 0.1568 ... 0.5309 0.2686\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:34.758903\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/2akx274x\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-510ee49b-07a2-4fbd-8d33-87bd4535e32f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-510ee49b-07a2-4fbd-8d33-87bd4535e32f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1658c1b9-dd71-4c3b-9893-c4a8d4d3eba0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1658c1b9-dd71-4c3b-9893-c4a8d4d3eba0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-40322be6-e7ec-460c-ac1e-80bdecdc5510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40322be6-e7ec-460c-ac1e-80bdecdc5510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b03cf0b-03ce-48b0-9356-e13ce16d287b' class='xr-var-data-in' type='checkbox'><label for='data-2b03cf0b-03ce-48b0-9356-e13ce16d287b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-42e25ca6-f49a-40e1-b44a-97fc6267bb49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42e25ca6-f49a-40e1-b44a-97fc6267bb49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-545dba7f-04b4-4e5c-a11d-f3d39560b19f' class='xr-var-data-in' type='checkbox'><label for='data-545dba7f-04b4-4e5c-a11d-f3d39560b19f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-226c1ed1-3079-40cc-a8b7-f31b0c916bd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-226c1ed1-3079-40cc-a8b7-f31b0c916bd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-039663fd-3c81-4c48-b240-9c968cb47f6c' class='xr-var-data-in' type='checkbox'><label for='data-039663fd-3c81-4c48-b240-9c968cb47f6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3cc4e9c-8aae-4842-a5d3-8c24df580909' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3cc4e9c-8aae-4842-a5d3-8c24df580909' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3674 0.3462 ... 0.3582 0.3258</div><input id='attrs-49db1c92-2b83-406f-b1b1-96ed29761da6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49db1c92-2b83-406f-b1b1-96ed29761da6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-998c8b35-55e5-456a-bb5a-0e8c54bfb07b' class='xr-var-data-in' type='checkbox'><label for='data-998c8b35-55e5-456a-bb5a-0e8c54bfb07b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.36737884, 0.34620211, 0.35404997, ..., 0.3812663 , 0.3812663 ,\n",
       "        0.36456829],\n",
       "       [0.31538387, 0.39894926, 0.40290999, ..., 0.3488265 , 0.41086626,\n",
       "        0.41180931],\n",
       "       [0.38595756, 0.38264033, 0.37089204, ..., 0.35779625, 0.33598826,\n",
       "        0.38444557],\n",
       "       [0.41773563, 0.37401869, 0.41959106, ..., 0.35294723, 0.35821587,\n",
       "        0.32582033]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_w</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2106 0.2189 ... 0.2551 0.2051</div><input id='attrs-0cbb4e09-4609-4da5-9e72-11f34a7698e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0cbb4e09-4609-4da5-9e72-11f34a7698e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b1bd894-19a5-4690-961b-e03652a46d5e' class='xr-var-data-in' type='checkbox'><label for='data-1b1bd894-19a5-4690-961b-e03652a46d5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.21062843, 0.21889712, 0.17484906, ..., 0.22176092, 0.22176092,\n",
       "        0.18259782],\n",
       "       [0.19967841, 0.21493451, 0.21234735, ..., 0.21966846, 0.16110492,\n",
       "        0.21234978],\n",
       "       [0.20022711, 0.18102296, 0.21480512, ..., 0.06544603, 0.24681061,\n",
       "        0.155301  ],\n",
       "       [0.21858219, 0.19430877, 0.1771009 , ..., 0.23790436, 0.25510559,\n",
       "        0.20511806]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1585 -0.1735 ... -0.06658</div><input id='attrs-99b0fd27-650a-4669-af71-4470cf12e599' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99b0fd27-650a-4669-af71-4470cf12e599' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786ce3de-f302-4e0e-91f0-07ee0b31c819' class='xr-var-data-in' type='checkbox'><label for='data-786ce3de-f302-4e0e-91f0-07ee0b31c819' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.15854913, -0.17345962, -0.06785621, ..., -0.11327401,\n",
       "        -0.11327401, -0.11132452],\n",
       "       [-0.15667417, -0.08644509, -0.07533449, ..., -0.0878155 ,\n",
       "        -0.08843206, -0.12135907],\n",
       "       [-0.21225429, -0.20687034, -0.16474684, ..., -0.15494929,\n",
       "        -0.11473091, -0.11724645],\n",
       "       [-0.15168655, -0.08983425, -0.13898508, ..., -0.17251345,\n",
       "        -0.10144137, -0.06657831]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_ws</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2024 -0.1429 ... -0.1958</div><input id='attrs-fe8f8c5c-04d5-428f-ab00-fcdecd2fbbd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe8f8c5c-04d5-428f-ab00-fcdecd2fbbd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7600268-1c0c-40af-ad8d-100570c38f33' class='xr-var-data-in' type='checkbox'><label for='data-d7600268-1c0c-40af-ad8d-100570c38f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.20238629, -0.14290348, -0.16487062, ..., -0.16412241,\n",
       "        -0.16412241, -0.12723506],\n",
       "       [-0.15511958, -0.12346563, -0.09802058, ..., -0.21816653,\n",
       "        -0.12170125, -0.07288244],\n",
       "       [-0.18133425, -0.17470242, -0.06289488, ..., -0.19408466,\n",
       "        -0.06894801, -0.1348593 ],\n",
       "       [-0.14435234, -0.17158033, -0.17570886, ..., -0.08950168,\n",
       "        -0.08581868, -0.19579144]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1466 0.1574 ... 0.1805 0.1231</div><input id='attrs-eed9ac38-bf86-4267-a1f9-1efa3dc33191' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eed9ac38-bf86-4267-a1f9-1efa3dc33191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaea02cd-2ce4-4a8a-b2ed-e35a4cb7fc41' class='xr-var-data-in' type='checkbox'><label for='data-eaea02cd-2ce4-4a8a-b2ed-e35a4cb7fc41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.14658476, 0.15740895, 0.13825705, ..., 0.11387529, 0.11387529,\n",
       "        0.12297724],\n",
       "       [0.16301564, 0.12139259, 0.12079071, ..., 0.12539729, 0.17596547,\n",
       "        0.14788003],\n",
       "       [0.18290035, 0.19606753, 0.1456577 , ..., 0.16628559, 0.15038235,\n",
       "        0.14735814],\n",
       "       [0.22796149, 0.12659552, 0.11663918, ..., 0.19655415, 0.18050289,\n",
       "        0.12313557]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1129 0.1568 ... 0.5309 0.2686</div><input id='attrs-a14a4c41-b33f-4fa1-bf38-5cdbe257313c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a14a4c41-b33f-4fa1-bf38-5cdbe257313c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bd278f6-df5d-4314-8093-161da15c7c43' class='xr-var-data-in' type='checkbox'><label for='data-0bd278f6-df5d-4314-8093-161da15c7c43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.11291325,  0.15675041,  0.20058757, ...,  0.93894269,\n",
       "          0.57800727,  0.21707185],\n",
       "        [ 0.15786113,  0.12730498,  0.09674883, ...,  0.88146233,\n",
       "          0.56509923,  0.24873613],\n",
       "        [ 0.0821865 ,  0.17920092,  0.27621533, ...,  0.76162586,\n",
       "          0.52889903,  0.2961722 ],\n",
       "        ...,\n",
       "        [ 0.10865698,  0.15950537,  0.21035377, ...,  0.88042364,\n",
       "          0.60302722,  0.3256308 ],\n",
       "        [ 0.10865698,  0.15950537,  0.21035377, ...,  0.88042364,\n",
       "          0.60302722,  0.3256308 ],\n",
       "        [ 0.16605993,  0.18197047,  0.19788101, ...,  0.78572569,\n",
       "          0.54716612,  0.30860654]],\n",
       "\n",
       "       [[ 0.11726005,  0.11570546,  0.11415086, ...,  0.82685603,\n",
       "          0.51506228,  0.20326852],\n",
       "        [ 0.14699422,  0.18401476,  0.22103529, ...,  0.82379449,\n",
       "          0.61388377,  0.40397305],\n",
       "        [ 0.16787655,  0.19056265,  0.21324874, ...,  0.78861241,\n",
       "          0.61525734,  0.44190227],\n",
       "...\n",
       "        [ 0.25321485,  0.29235022,  0.33148559, ...,  0.77227623,\n",
       "          0.42324228,  0.07420832],\n",
       "        [ 0.13496055,  0.08917765,  0.04339474, ...,  0.76647779,\n",
       "          0.58279886,  0.39911994],\n",
       "        [ 0.21153171,  0.22914457,  0.24675742, ...,  0.79185232,\n",
       "          0.53974657,  0.28764083]],\n",
       "\n",
       "       [[ 0.20648764,  0.19915344,  0.19181924, ...,  0.93235671,\n",
       "          0.63631782,  0.34027893],\n",
       "        [ 0.09796384,  0.17970992,  0.261456  , ...,  0.82974203,\n",
       "          0.56832746,  0.30691288],\n",
       "        [ 0.20576639,  0.24249017,  0.27921395, ...,  0.9113859 ,\n",
       "          0.59669196,  0.28199802],\n",
       "        ...,\n",
       "        [ 0.19805463,  0.11504287,  0.0320311 , ...,  0.85286672,\n",
       "          0.59085159,  0.32883646],\n",
       "        [ 0.11873297,  0.10311028,  0.08748759, ...,  0.8005815 ,\n",
       "          0.61332145,  0.42606141],\n",
       "        [-0.00851086,  0.12070227,  0.24991539, ...,  0.79330814,\n",
       "          0.53093839,  0.26856864]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d3136fd-ff15-4144-ae66-7a291c1c4701' class='xr-section-summary-in' type='checkbox'  ><label for='section-9d3136fd-ff15-4144-ae66-7a291c1c4701' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f11d0227-a270-4280-a846-47a04d0018b0' class='xr-index-data-in' type='checkbox'/><label for='index-f11d0227-a270-4280-a846-47a04d0018b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4f4a4570-f194-48e3-b17b-bb2e5fd22309' class='xr-index-data-in' type='checkbox'/><label for='index-4f4a4570-f194-48e3-b17b-bb2e5fd22309' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fbc48e3b-e8ce-4611-9241-aa6ded4c1c63' class='xr-index-data-in' type='checkbox'/><label for='index-fbc48e3b-e8ce-4611-9241-aa6ded4c1c63' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e4ff7c3-200d-4dbd-a54e-2ca261d8ae42' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e4ff7c3-200d-4dbd-a54e-2ca261d8ae42' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:34.758903</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/2akx274x</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real beta_ws;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent + beta_ws * water_cent .* shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        beta_ws ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior_predictive9c407283-9c11-4765-a8fa-db964a90e5b4\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior_predictive9c407283-9c11-4765-a8fa-db964a90e5b4\" class = \"xr-section-summary\">posterior_predictive</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "  font-size: 11px;\n",
       "  width: 15px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
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       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
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       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
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       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (chain: 4, draw: 1000, blooms_std_hat_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain                 (chain) int64 0 1 2 3\n",
       "  * draw                  (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * blooms_std_hat_dim_0  (blooms_std_hat_dim_0) int64 0 1 2 3 4 ... 23 24 25 26\n",
       "Data variables:\n",
       "    blooms_std_hat        (chain, draw, blooms_std_hat_dim_0) float64 0.04633...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:34.902871\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f80f0b33-fce4-4dbe-9cb9-63e1d59fdfd2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f80f0b33-fce4-4dbe-9cb9-63e1d59fdfd2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>blooms_std_hat_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2bf6beea-bb8c-4afb-9186-8bddffa4af05' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2bf6beea-bb8c-4afb-9186-8bddffa4af05' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-00fd72cf-daf9-45d9-ad17-0ca79735d01f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00fd72cf-daf9-45d9-ad17-0ca79735d01f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0ce6aa9-c2ba-4174-a9b6-ef7ffcd696c8' class='xr-var-data-in' type='checkbox'><label for='data-b0ce6aa9-c2ba-4174-a9b6-ef7ffcd696c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-16eef4b6-542a-4883-b189-e6c92eab2b57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16eef4b6-542a-4883-b189-e6c92eab2b57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e89981c2-8b50-4a93-96c8-51616c31e2bd' class='xr-var-data-in' type='checkbox'><label for='data-e89981c2-8b50-4a93-96c8-51616c31e2bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>blooms_std_hat_dim_0</span></div><div class='xr-var-dims'>(blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-42ac2102-574d-42a9-8859-1a5448173632' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42ac2102-574d-42a9-8859-1a5448173632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-476a753d-4669-4071-812e-2c600072ca8a' class='xr-var-data-in' type='checkbox'><label for='data-476a753d-4669-4071-812e-2c600072ca8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-876953b4-3f60-47a2-bd3a-60b93678e484' class='xr-section-summary-in' type='checkbox'  checked><label for='section-876953b4-3f60-47a2-bd3a-60b93678e484' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blooms_std_hat</span></div><div class='xr-var-dims'>(chain, draw, blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04633 0.02477 ... 0.3828 0.267</div><input id='attrs-efdefca7-071d-4f7c-86c3-f5ef941d5127' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efdefca7-071d-4f7c-86c3-f5ef941d5127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-528f9cf6-90eb-49de-8db2-e6eaa43de025' class='xr-var-data-in' type='checkbox'><label for='data-528f9cf6-90eb-49de-8db2-e6eaa43de025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.0463254 ,  0.02476859,  0.37320187, ...,  0.90523212,\n",
       "          0.53011558,  0.09205915],\n",
       "        [ 0.29186008,  0.18885816, -0.06106734, ...,  0.9816119 ,\n",
       "          0.63169854,  0.0632436 ],\n",
       "        [ 0.29798341,  0.284066  ,  0.11397076, ...,  0.76958088,\n",
       "          0.50548862,  0.31404787],\n",
       "        ...,\n",
       "        [ 0.19944635,  0.25161844,  0.0525404 , ...,  0.70112669,\n",
       "          0.54008173,  0.31415059],\n",
       "        [ 0.34089784,  0.12748473, -0.02816593, ...,  0.91616261,\n",
       "          0.53619877,  0.22383521],\n",
       "        [ 0.00732367, -0.06633766,  0.3284374 , ...,  0.86417746,\n",
       "          0.63078456,  0.14567097]],\n",
       "\n",
       "       [[ 0.05561144,  0.23044961,  0.24209029, ...,  0.85643235,\n",
       "          0.49810582,  0.25812187],\n",
       "        [ 0.1150434 ,  0.21979565,  0.37690305, ...,  0.89476205,\n",
       "          0.60947367,  0.22322956],\n",
       "        [ 0.17246855,  0.20896049,  0.26490137, ...,  0.90115122,\n",
       "          0.66142092,  0.37014357],\n",
       "...\n",
       "        [ 0.10007018,  0.49877514,  0.19607989, ...,  0.49758795,\n",
       "          0.42841612, -0.03845415],\n",
       "        [-0.04559483,  0.08430191,  0.1650598 , ...,  0.77091817,\n",
       "          0.61079065,  0.19595267],\n",
       "        [ 0.26111839, -0.06284362,  0.21962989, ...,  0.74521217,\n",
       "          0.57775821,  0.19224851]],\n",
       "\n",
       "       [[ 0.51102297,  0.34607688,  0.15661581, ...,  1.04385769,\n",
       "          0.68044627,  0.12178892],\n",
       "        [ 0.03056122,  0.44743767,  0.25100855, ...,  0.89541569,\n",
       "          0.64525104,  0.40298881],\n",
       "        [ 0.36028514,  0.18434664,  0.27400212, ...,  0.9496615 ,\n",
       "          0.77877545,  0.19376282],\n",
       "        ...,\n",
       "        [ 0.36627742,  0.17552147, -0.09645325, ...,  0.94831458,\n",
       "          0.39805783,  0.48319151],\n",
       "        [ 0.24708457,  0.14138798,  0.05353603, ...,  0.67258316,\n",
       "          0.51990772,  0.11513807],\n",
       "        [ 0.09969633,  0.25706263,  0.39136851, ...,  0.7927707 ,\n",
       "          0.3828408 ,  0.26701288]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e37a36ef-f5ae-4396-8f94-682398bccebe' class='xr-section-summary-in' type='checkbox'  ><label for='section-e37a36ef-f5ae-4396-8f94-682398bccebe' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-43d2dee0-ae79-438f-a6be-2ca80ee2d209' class='xr-index-data-in' type='checkbox'/><label for='index-43d2dee0-ae79-438f-a6be-2ca80ee2d209' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aaa3ced1-8268-457b-9a84-187296a2ef03' class='xr-index-data-in' type='checkbox'/><label for='index-aaa3ced1-8268-457b-9a84-187296a2ef03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>blooms_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d3b95802-4bae-4136-880b-4014e4d8f757' class='xr-index-data-in' type='checkbox'/><label for='index-d3b95802-4bae-4136-880b-4014e4d8f757' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;blooms_std_hat_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-503cdc25-1a0d-4687-a124-4a72bf8efdd5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-503cdc25-1a0d-4687-a124-4a72bf8efdd5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:34.902871</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihood400eab54-b0a9-4cf2-ae3a-046e178548f7\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihood400eab54-b0a9-4cf2-ae3a-046e178548f7\" class = \"xr-section-summary\">log_likelihood</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (chain: 4, draw: 1000, log_lik_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain          (chain) int64 0 1 2 3\n",
       "  * draw           (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_lik_dim_0  (log_lik_dim_0) int64 0 1 2 3 4 5 6 ... 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    log_lik        (chain, draw, log_lik_dim_0) float64 0.7045 0.4295 ... 0.8218\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:34.858158\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e8fff49e-10b0-487c-83cc-0bfb16694d97' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e8fff49e-10b0-487c-83cc-0bfb16694d97' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7861d6e8-acd7-4fe6-8183-10afc97ca966' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7861d6e8-acd7-4fe6-8183-10afc97ca966' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-94d76107-19cd-4a7e-a97d-4bbd5a18c578' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94d76107-19cd-4a7e-a97d-4bbd5a18c578' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf621af-5bd1-49f7-8f90-c97bd0fdcae5' class='xr-var-data-in' type='checkbox'><label for='data-dbf621af-5bd1-49f7-8f90-c97bd0fdcae5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-28ec9533-7871-49e0-9c54-aee21ae969e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28ec9533-7871-49e0-9c54-aee21ae969e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3eb954a-44b3-46b8-887d-bdfa1da92eb3' class='xr-var-data-in' type='checkbox'><label for='data-a3eb954a-44b3-46b8-887d-bdfa1da92eb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-27433ea7-3752-4e96-ae78-0fb19012b880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27433ea7-3752-4e96-ae78-0fb19012b880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53ff5c02-f5ab-4e03-b34e-4e367fb9484e' class='xr-var-data-in' type='checkbox'><label for='data-53ff5c02-f5ab-4e03-b34e-4e367fb9484e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4dd70dad-bef3-4630-8818-8c72cb63647f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4dd70dad-bef3-4630-8818-8c72cb63647f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7045 0.4295 ... 0.3474 0.8218</div><input id='attrs-1c888dd7-3bad-42c1-896f-ea9c7b451988' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c888dd7-3bad-42c1-896f-ea9c7b451988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9b43329-02e0-45db-9418-54f2174cc2ea' class='xr-var-data-in' type='checkbox'><label for='data-d9b43329-02e0-45db-9418-54f2174cc2ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 7.04537073e-01,  4.29458233e-01,  7.37572896e-01, ...,\n",
       "          7.82554600e-01,  7.12450611e-01,  4.41627749e-01],\n",
       "        [ 4.27092784e-01,  6.02928737e-01,  3.76784708e-02, ...,\n",
       "          8.98554494e-01,  6.18159847e-01,  6.22639744e-01],\n",
       "        [ 8.83018438e-01,  2.19708699e-01,  1.03486656e+00, ...,\n",
       "          8.90698423e-01,  3.85834422e-01,  9.08724349e-01],\n",
       "        ...,\n",
       "        [ 7.98487698e-01,  2.72729483e-01,  8.93351013e-01, ...,\n",
       "          1.19680588e+00,  9.66033175e-01,  1.17029018e+00],\n",
       "        [ 7.98487698e-01,  2.72729483e-01,  8.93351013e-01, ...,\n",
       "          1.19680588e+00,  9.66033175e-01,  1.17029018e+00],\n",
       "        [ 2.65121099e-01,  8.20488074e-02,  7.82950418e-01, ...,\n",
       "          1.07209540e+00,  5.07929155e-01,  1.04334385e+00]],\n",
       "\n",
       "       [[ 6.36261559e-01,  6.43075833e-01,  1.95005941e-01, ...,\n",
       "          8.90651892e-01,  3.23075746e-01,  3.58370997e-01],\n",
       "        [ 4.56648159e-01,  4.08638828e-02,  9.38878020e-01, ...,\n",
       "          1.17853326e+00,  9.96270845e-01,  1.15541503e+00],\n",
       "        [ 2.28966654e-01, -4.96956389e-02,  8.93371429e-01, ...,\n",
       "          1.09705926e+00,  1.00635301e+00,  1.02800172e+00],\n",
       "...\n",
       "        [-2.84305587e-01, -6.70385111e-01,  8.64294119e-01, ...,\n",
       "          7.87187336e-01, -4.05902351e-01, -7.30015634e-01],\n",
       "        [ 5.72927977e-01,  7.99807598e-01, -5.61028514e-01, ...,\n",
       "          8.49511565e-01,  7.24368447e-01,  9.59548004e-01],\n",
       "        [-3.43706430e-02, -2.13089796e-01,  9.12305310e-01, ...,\n",
       "          9.38030275e-01,  4.80222819e-01,  8.31521531e-01]],\n",
       "\n",
       "       [[ 1.49402712e-01,  1.78027470e-01,  4.31930485e-01, ...,\n",
       "          4.81097061e-01,  5.32524573e-01,  5.49869737e-01],\n",
       "        [ 8.48410536e-01,  1.40244506e-01,  1.08302440e+00, ...,\n",
       "          1.14312696e+00,  6.90461570e-01,  1.01506251e+00],\n",
       "        [-3.26341405e-01, -9.31341013e-01,  1.20129791e+00, ...,\n",
       "          1.05282466e+00,  9.13825958e-01,  9.31066379e-01],\n",
       "        ...,\n",
       "        [ 2.00215705e-01,  5.36591551e-01, -2.70852287e-01, ...,\n",
       "          7.06352253e-01,  5.82176321e-01,  6.83604004e-01],\n",
       "        [ 5.76726184e-01,  6.29912974e-01,  5.34077970e-02, ...,\n",
       "          7.66735584e-01,  7.04236498e-01,  7.48458948e-01],\n",
       "        [ 1.17314220e+00,  6.95096737e-01,  1.06796417e+00, ...,\n",
       "          1.09732698e+00,  3.47445117e-01,  8.21757429e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c4dbf48-4fd9-4ee3-ba0a-2e4508033628' class='xr-section-summary-in' type='checkbox'  ><label for='section-2c4dbf48-4fd9-4ee3-ba0a-2e4508033628' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0252bfc2-0898-4545-b47c-3f71e4d125cb' class='xr-index-data-in' type='checkbox'/><label for='index-0252bfc2-0898-4545-b47c-3f71e4d125cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e128e64e-917b-4e66-83df-3c02a8549ae0' class='xr-index-data-in' type='checkbox'/><label for='index-e128e64e-917b-4e66-83df-3c02a8549ae0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec7b1d05-abc0-4271-991d-376b0b78de96' class='xr-index-data-in' type='checkbox'/><label for='index-ec7b1d05-abc0-4271-991d-376b0b78de96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26a6f56b-d5e2-4f29-b092-de5a661b2328' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26a6f56b-d5e2-4f29-b092-de5a661b2328' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:34.858158</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
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       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsaf8283b7-9a74-4eb0-b4f0-ede3801a929a\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsaf8283b7-9a74-4eb0-b4f0-ede3801a929a\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
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       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header {\n",
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       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
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       "\n",
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       "  display: grid;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
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       "\n",
       ".xr-section-summary > span {\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "  font-size: 11px;\n",
       "  width: 15px;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
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       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
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       "\n",
       ".xr-array-data,\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
       ".xr-dim-list {\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
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       "\n",
       ".xr-var-item > div,\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "  font-weight: normal;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    acceptance_rate  (chain, draw) float64 0.9824 0.9818 ... 0.9986 0.9993\n",
       "    step_size        (chain, draw) float64 0.7264 0.7264 ... 0.6313 0.6313\n",
       "    tree_depth       (chain, draw) int64 2 2 3 3 2 2 3 3 2 ... 3 3 2 3 3 2 3 3 3\n",
       "    n_steps          (chain, draw) int64 7 3 7 7 3 3 7 7 3 ... 7 7 3 7 7 3 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -36.43 -35.68 ... -32.65 -33.83\n",
       "    lp               (chain, draw) float64 37.17 37.08 37.86 ... 35.73 36.25\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:34.810343\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/2akx274x\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b2afe8c4-3034-4083-a32e-c601388a5cf5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2afe8c4-3034-4083-a32e-c601388a5cf5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bff8a7b6-c113-4070-bc37-f9e98c9a66c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bff8a7b6-c113-4070-bc37-f9e98c9a66c4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-8ccf379a-6095-49f7-8323-a84bbd183baf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ccf379a-6095-49f7-8323-a84bbd183baf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb906e6-2ea3-47f8-b582-c6662875b194' class='xr-var-data-in' type='checkbox'><label for='data-fcb906e6-2ea3-47f8-b582-c6662875b194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-b754939d-a937-464c-acf7-5d70037c4fe3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b754939d-a937-464c-acf7-5d70037c4fe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba76696-0615-4b93-944e-23d77ebb4cc3' class='xr-var-data-in' type='checkbox'><label for='data-3ba76696-0615-4b93-944e-23d77ebb4cc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2f1f58b-1bcb-41dc-b113-fe7ab6576bce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2f1f58b-1bcb-41dc-b113-fe7ab6576bce' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9824 0.9818 ... 0.9986 0.9993</div><input id='attrs-75ec5eda-086d-44ef-a4d4-ff189d7b38e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75ec5eda-086d-44ef-a4d4-ff189d7b38e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2475d74-8c9e-4722-838a-30651d42c403' class='xr-var-data-in' type='checkbox'><label for='data-c2475d74-8c9e-4722-838a-30651d42c403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.98235854, 0.98181903, 0.91394172, ..., 1.        , 0.68418114,\n",
       "        1.        ],\n",
       "       [0.8580094 , 0.97349576, 0.92912702, ..., 0.92227392, 0.93014761,\n",
       "        0.98513292],\n",
       "       [0.99421326, 0.98115494, 0.99593533, ..., 0.88496238, 1.        ,\n",
       "        0.98099848],\n",
       "       [0.99067039, 0.8919415 , 0.62481845, ..., 0.71084799, 0.99855068,\n",
       "        0.9993465 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7264 0.7264 ... 0.6313 0.6313</div><input id='attrs-20cd7c7c-cfce-4137-a562-48102a9d459d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20cd7c7c-cfce-4137-a562-48102a9d459d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a558530-d4e3-4bf8-9095-400c7acc67cf' class='xr-var-data-in' type='checkbox'><label for='data-9a558530-d4e3-4bf8-9095-400c7acc67cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.72643421, 0.72643421, 0.72643421, ..., 0.72643421, 0.72643421,\n",
       "        0.72643421],\n",
       "       [0.56420716, 0.56420716, 0.56420716, ..., 0.56420716, 0.56420716,\n",
       "        0.56420716],\n",
       "       [0.54673893, 0.54673893, 0.54673893, ..., 0.54673893, 0.54673893,\n",
       "        0.54673893],\n",
       "       [0.63127613, 0.63127613, 0.63127613, ..., 0.63127613, 0.63127613,\n",
       "        0.63127613]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 3 3 2 2 3 3 ... 3 2 3 3 2 3 3 3</div><input id='attrs-989ff5ea-405e-4d81-9425-acacc97372ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-989ff5ea-405e-4d81-9425-acacc97372ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bba72471-d4e3-4604-90e1-add9c6484347' class='xr-var-data-in' type='checkbox'><label for='data-bba72471-d4e3-4604-90e1-add9c6484347' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 3, ..., 2, 2, 3],\n",
       "       [3, 3, 2, ..., 3, 3, 3],\n",
       "       [3, 2, 3, ..., 2, 3, 3],\n",
       "       [2, 3, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>7 3 7 7 3 3 7 7 ... 7 3 7 7 3 7 7 7</div><input id='attrs-e1371bdf-92cd-41e7-ab6d-7c7bb592ad84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1371bdf-92cd-41e7-ab6d-7c7bb592ad84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e67fc82f-b1fb-4fe7-946d-9941971b79ed' class='xr-var-data-in' type='checkbox'><label for='data-e67fc82f-b1fb-4fe7-946d-9941971b79ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7, 3, 7, ..., 3, 3, 7],\n",
       "       [7, 7, 3, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 3, 7, 7],\n",
       "       [3, 7, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2b4faa8e-6f81-4193-880f-c190d2ac3a41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b4faa8e-6f81-4193-880f-c190d2ac3a41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57c7e362-124e-444e-baaa-1b4f8abf6e5a' class='xr-var-data-in' type='checkbox'><label for='data-57c7e362-124e-444e-baaa-1b4f8abf6e5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-36.43 -35.68 ... -32.65 -33.83</div><input id='attrs-18e36689-6167-48f2-bd0d-ab0bbc04aca7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18e36689-6167-48f2-bd0d-ab0bbc04aca7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9673a330-45ff-4e38-b7b8-a3d64bb1052e' class='xr-var-data-in' type='checkbox'><label for='data-9673a330-45ff-4e38-b7b8-a3d64bb1052e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-36.43421644, -35.68436731, -34.09173843, ..., -35.79997085,\n",
       "        -35.47138861, -37.98232571],\n",
       "       [-34.01217835, -34.31608931, -35.5817405 , ..., -33.76478259,\n",
       "        -33.40508759, -33.43048778],\n",
       "       [-32.53738813, -32.27646822, -30.65757325, ..., -29.57387547,\n",
       "        -29.37625166, -33.91819817],\n",
       "       [-31.95874109, -28.00690724, -32.28058677, ..., -30.87933265,\n",
       "        -32.64867508, -33.82932667]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>37.17 37.08 37.86 ... 35.73 36.25</div><input id='attrs-a786851e-e9f4-4b6c-ae91-cea45fe9fa19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a786851e-e9f4-4b6c-ae91-cea45fe9fa19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37bda780-146a-474d-80b1-78dfac0bc969' class='xr-var-data-in' type='checkbox'><label for='data-37bda780-146a-474d-80b1-78dfac0bc969' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[37.17476922, 37.08022354, 37.85707771, ..., 38.37988623,\n",
       "        38.37988623, 39.12037867],\n",
       "       [36.56589271, 37.37729947, 35.89476304, ..., 36.90054186,\n",
       "        35.43987565, 35.93778519],\n",
       "       [33.48980878, 33.18306489, 36.08926564, ..., 30.3703066 ,\n",
       "        36.53647616, 37.61992715],\n",
       "       [32.14811601, 38.71834847, 34.12871723, ..., 34.24667719,\n",
       "        35.73270184, 36.25401504]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de2020d2-cf6f-45da-a398-7fd595226780' class='xr-section-summary-in' type='checkbox'  ><label for='section-de2020d2-cf6f-45da-a398-7fd595226780' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-30dc33f1-ab81-4379-8147-6f3f6fa10adf' class='xr-index-data-in' type='checkbox'/><label for='index-30dc33f1-ab81-4379-8147-6f3f6fa10adf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0bf70c0e-ae29-4792-9379-0aee8b41a100' class='xr-index-data-in' type='checkbox'/><label for='index-0bf70c0e-ae29-4792-9379-0aee8b41a100' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ef60a24-f7dc-4349-9eef-2ad0a115334a' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ef60a24-f7dc-4349-9eef-2ad0a115334a' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:34.810343</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/2akx274x</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real beta_ws;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent + beta_ws * water_cent .* shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        beta_ws ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior689856a0-60b4-4f7a-bf75-01736cb8413a\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_prior689856a0-60b4-4f7a-bf75-01736cb8413a\" class = \"xr-section-summary\">prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (chain: 4, draw: 1000, mu_dim_0: 27,\n",
       "                           log_lik_dim_0: 27, blooms_std_hat_dim_0: 27)\n",
       "Coordinates:\n",
       "  * chain                 (chain) int64 0 1 2 3\n",
       "  * draw                  (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * mu_dim_0              (mu_dim_0) int64 0 1 2 3 4 5 6 ... 21 22 23 24 25 26\n",
       "  * log_lik_dim_0         (log_lik_dim_0) int64 0 1 2 3 4 5 ... 22 23 24 25 26\n",
       "  * blooms_std_hat_dim_0  (blooms_std_hat_dim_0) int64 0 1 2 3 4 ... 23 24 25 26\n",
       "Data variables:\n",
       "    alpha                 (chain, draw) float64 0.3674 0.3462 ... 0.3582 0.3258\n",
       "    beta_w                (chain, draw) float64 0.2106 0.2189 ... 0.2551 0.2051\n",
       "    beta_s                (chain, draw) float64 -0.1585 -0.1735 ... -0.06658\n",
       "    beta_ws               (chain, draw) float64 -0.2024 -0.1429 ... -0.1958\n",
       "    sigma                 (chain, draw) float64 0.1466 0.1574 ... 0.1805 0.1231\n",
       "    mu                    (chain, draw, mu_dim_0) float64 0.1129 ... 0.2686\n",
       "    log_lik               (chain, draw, log_lik_dim_0) float64 0.7045 ... 0.8218\n",
       "    blooms_std_hat        (chain, draw, blooms_std_hat_dim_0) float64 0.04633...\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:34.950864\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/2akx274x\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7386c544-e668-4e55-8db5-7e3a55ae8cc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7386c544-e668-4e55-8db5-7e3a55ae8cc5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>mu_dim_0</span>: 27</li><li><span class='xr-has-index'>log_lik_dim_0</span>: 27</li><li><span class='xr-has-index'>blooms_std_hat_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-18f20644-654e-4b28-a094-432a40cf551e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18f20644-654e-4b28-a094-432a40cf551e' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e4f15758-4ff4-4123-bcc6-134d321f727a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4f15758-4ff4-4123-bcc6-134d321f727a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7524a3d8-47a6-430b-8cc9-49498e7808ab' class='xr-var-data-in' type='checkbox'><label for='data-7524a3d8-47a6-430b-8cc9-49498e7808ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-f70d1a56-6f78-46e4-a146-c4b64c087c08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f70d1a56-6f78-46e4-a146-c4b64c087c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ac3b706-460e-4951-8e46-0577610c9f0c' class='xr-var-data-in' type='checkbox'><label for='data-1ac3b706-460e-4951-8e46-0577610c9f0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-22fca2de-8288-482c-bacd-a1591fa67ebd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22fca2de-8288-482c-bacd-a1591fa67ebd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c81f01e-983b-4891-94b7-8caa0b41e01c' class='xr-var-data-in' type='checkbox'><label for='data-5c81f01e-983b-4891-94b7-8caa0b41e01c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_lik_dim_0</span></div><div class='xr-var-dims'>(log_lik_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-828866a7-99d3-4a1b-bd8e-30ae6be66f2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-828866a7-99d3-4a1b-bd8e-30ae6be66f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7d1555d-1ada-43a0-b412-84b6c035a9fb' class='xr-var-data-in' type='checkbox'><label for='data-a7d1555d-1ada-43a0-b412-84b6c035a9fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>blooms_std_hat_dim_0</span></div><div class='xr-var-dims'>(blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-8f8c42fc-91e5-4f52-9a9d-5b6216f75933' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f8c42fc-91e5-4f52-9a9d-5b6216f75933' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83fcafb1-35ef-4277-b29c-c6c251ee4390' class='xr-var-data-in' type='checkbox'><label for='data-83fcafb1-35ef-4277-b29c-c6c251ee4390' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14f6320a-0672-4a6e-91bf-8b8e9a2c80da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14f6320a-0672-4a6e-91bf-8b8e9a2c80da' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3674 0.3462 ... 0.3582 0.3258</div><input id='attrs-02355c43-8100-4df3-afb3-b03da8db4090' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02355c43-8100-4df3-afb3-b03da8db4090' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-505dda02-3a18-49f2-834a-abb5d014a227' class='xr-var-data-in' type='checkbox'><label for='data-505dda02-3a18-49f2-834a-abb5d014a227' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.36737884, 0.34620211, 0.35404997, ..., 0.3812663 , 0.3812663 ,\n",
       "        0.36456829],\n",
       "       [0.31538387, 0.39894926, 0.40290999, ..., 0.3488265 , 0.41086626,\n",
       "        0.41180931],\n",
       "       [0.38595756, 0.38264033, 0.37089204, ..., 0.35779625, 0.33598826,\n",
       "        0.38444557],\n",
       "       [0.41773563, 0.37401869, 0.41959106, ..., 0.35294723, 0.35821587,\n",
       "        0.32582033]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_w</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2106 0.2189 ... 0.2551 0.2051</div><input id='attrs-7d8fa1dc-535c-4c45-be53-2a4a80ea7c94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d8fa1dc-535c-4c45-be53-2a4a80ea7c94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b07846a0-c449-46f1-962b-faa2ac327547' class='xr-var-data-in' type='checkbox'><label for='data-b07846a0-c449-46f1-962b-faa2ac327547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.21062843, 0.21889712, 0.17484906, ..., 0.22176092, 0.22176092,\n",
       "        0.18259782],\n",
       "       [0.19967841, 0.21493451, 0.21234735, ..., 0.21966846, 0.16110492,\n",
       "        0.21234978],\n",
       "       [0.20022711, 0.18102296, 0.21480512, ..., 0.06544603, 0.24681061,\n",
       "        0.155301  ],\n",
       "       [0.21858219, 0.19430877, 0.1771009 , ..., 0.23790436, 0.25510559,\n",
       "        0.20511806]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_s</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1585 -0.1735 ... -0.06658</div><input id='attrs-4c2926ec-10b2-4004-be5c-7e1cd670320e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c2926ec-10b2-4004-be5c-7e1cd670320e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e43f43f-ddf4-4c92-87ad-f2b39c64891a' class='xr-var-data-in' type='checkbox'><label for='data-5e43f43f-ddf4-4c92-87ad-f2b39c64891a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.15854913, -0.17345962, -0.06785621, ..., -0.11327401,\n",
       "        -0.11327401, -0.11132452],\n",
       "       [-0.15667417, -0.08644509, -0.07533449, ..., -0.0878155 ,\n",
       "        -0.08843206, -0.12135907],\n",
       "       [-0.21225429, -0.20687034, -0.16474684, ..., -0.15494929,\n",
       "        -0.11473091, -0.11724645],\n",
       "       [-0.15168655, -0.08983425, -0.13898508, ..., -0.17251345,\n",
       "        -0.10144137, -0.06657831]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta_ws</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.2024 -0.1429 ... -0.1958</div><input id='attrs-cba17847-5a4f-473e-8660-6361b1b82aec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cba17847-5a4f-473e-8660-6361b1b82aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-100051f9-04a3-4914-846e-8b6e412055cf' class='xr-var-data-in' type='checkbox'><label for='data-100051f9-04a3-4914-846e-8b6e412055cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.20238629, -0.14290348, -0.16487062, ..., -0.16412241,\n",
       "        -0.16412241, -0.12723506],\n",
       "       [-0.15511958, -0.12346563, -0.09802058, ..., -0.21816653,\n",
       "        -0.12170125, -0.07288244],\n",
       "       [-0.18133425, -0.17470242, -0.06289488, ..., -0.19408466,\n",
       "        -0.06894801, -0.1348593 ],\n",
       "       [-0.14435234, -0.17158033, -0.17570886, ..., -0.08950168,\n",
       "        -0.08581868, -0.19579144]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1466 0.1574 ... 0.1805 0.1231</div><input id='attrs-ed43dbf9-579c-4c33-86e4-4d428ea494f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed43dbf9-579c-4c33-86e4-4d428ea494f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c9e6ac1-91eb-4daa-a957-735bff6550df' class='xr-var-data-in' type='checkbox'><label for='data-9c9e6ac1-91eb-4daa-a957-735bff6550df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.14658476, 0.15740895, 0.13825705, ..., 0.11387529, 0.11387529,\n",
       "        0.12297724],\n",
       "       [0.16301564, 0.12139259, 0.12079071, ..., 0.12539729, 0.17596547,\n",
       "        0.14788003],\n",
       "       [0.18290035, 0.19606753, 0.1456577 , ..., 0.16628559, 0.15038235,\n",
       "        0.14735814],\n",
       "       [0.22796149, 0.12659552, 0.11663918, ..., 0.19655415, 0.18050289,\n",
       "        0.12313557]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw, mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1129 0.1568 ... 0.5309 0.2686</div><input id='attrs-08eead68-857d-4471-a0e7-66205b85b6d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08eead68-857d-4471-a0e7-66205b85b6d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bda1d101-d688-4c6e-b4fd-aef12498f1be' class='xr-var-data-in' type='checkbox'><label for='data-bda1d101-d688-4c6e-b4fd-aef12498f1be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.11291325,  0.15675041,  0.20058757, ...,  0.93894269,\n",
       "          0.57800727,  0.21707185],\n",
       "        [ 0.15786113,  0.12730498,  0.09674883, ...,  0.88146233,\n",
       "          0.56509923,  0.24873613],\n",
       "        [ 0.0821865 ,  0.17920092,  0.27621533, ...,  0.76162586,\n",
       "          0.52889903,  0.2961722 ],\n",
       "        ...,\n",
       "        [ 0.10865698,  0.15950537,  0.21035377, ...,  0.88042364,\n",
       "          0.60302722,  0.3256308 ],\n",
       "        [ 0.10865698,  0.15950537,  0.21035377, ...,  0.88042364,\n",
       "          0.60302722,  0.3256308 ],\n",
       "        [ 0.16605993,  0.18197047,  0.19788101, ...,  0.78572569,\n",
       "          0.54716612,  0.30860654]],\n",
       "\n",
       "       [[ 0.11726005,  0.11570546,  0.11415086, ...,  0.82685603,\n",
       "          0.51506228,  0.20326852],\n",
       "        [ 0.14699422,  0.18401476,  0.22103529, ...,  0.82379449,\n",
       "          0.61388377,  0.40397305],\n",
       "        [ 0.16787655,  0.19056265,  0.21324874, ...,  0.78861241,\n",
       "          0.61525734,  0.44190227],\n",
       "...\n",
       "        [ 0.25321485,  0.29235022,  0.33148559, ...,  0.77227623,\n",
       "          0.42324228,  0.07420832],\n",
       "        [ 0.13496055,  0.08917765,  0.04339474, ...,  0.76647779,\n",
       "          0.58279886,  0.39911994],\n",
       "        [ 0.21153171,  0.22914457,  0.24675742, ...,  0.79185232,\n",
       "          0.53974657,  0.28764083]],\n",
       "\n",
       "       [[ 0.20648764,  0.19915344,  0.19181924, ...,  0.93235671,\n",
       "          0.63631782,  0.34027893],\n",
       "        [ 0.09796384,  0.17970992,  0.261456  , ...,  0.82974203,\n",
       "          0.56832746,  0.30691288],\n",
       "        [ 0.20576639,  0.24249017,  0.27921395, ...,  0.9113859 ,\n",
       "          0.59669196,  0.28199802],\n",
       "        ...,\n",
       "        [ 0.19805463,  0.11504287,  0.0320311 , ...,  0.85286672,\n",
       "          0.59085159,  0.32883646],\n",
       "        [ 0.11873297,  0.10311028,  0.08748759, ...,  0.8005815 ,\n",
       "          0.61332145,  0.42606141],\n",
       "        [-0.00851086,  0.12070227,  0.24991539, ...,  0.79330814,\n",
       "          0.53093839,  0.26856864]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, log_lik_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7045 0.4295 ... 0.3474 0.8218</div><input id='attrs-2e50668c-83dd-42a9-aaed-083082d0d85d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e50668c-83dd-42a9-aaed-083082d0d85d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905b3a66-48ec-45c5-90c7-e3475fc12500' class='xr-var-data-in' type='checkbox'><label for='data-905b3a66-48ec-45c5-90c7-e3475fc12500' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 7.04537073e-01,  4.29458233e-01,  7.37572896e-01, ...,\n",
       "          7.82554600e-01,  7.12450611e-01,  4.41627749e-01],\n",
       "        [ 4.27092784e-01,  6.02928737e-01,  3.76784708e-02, ...,\n",
       "          8.98554494e-01,  6.18159847e-01,  6.22639744e-01],\n",
       "        [ 8.83018438e-01,  2.19708699e-01,  1.03486656e+00, ...,\n",
       "          8.90698423e-01,  3.85834422e-01,  9.08724349e-01],\n",
       "        ...,\n",
       "        [ 7.98487698e-01,  2.72729483e-01,  8.93351013e-01, ...,\n",
       "          1.19680588e+00,  9.66033175e-01,  1.17029018e+00],\n",
       "        [ 7.98487698e-01,  2.72729483e-01,  8.93351013e-01, ...,\n",
       "          1.19680588e+00,  9.66033175e-01,  1.17029018e+00],\n",
       "        [ 2.65121099e-01,  8.20488074e-02,  7.82950418e-01, ...,\n",
       "          1.07209540e+00,  5.07929155e-01,  1.04334385e+00]],\n",
       "\n",
       "       [[ 6.36261559e-01,  6.43075833e-01,  1.95005941e-01, ...,\n",
       "          8.90651892e-01,  3.23075746e-01,  3.58370997e-01],\n",
       "        [ 4.56648159e-01,  4.08638828e-02,  9.38878020e-01, ...,\n",
       "          1.17853326e+00,  9.96270845e-01,  1.15541503e+00],\n",
       "        [ 2.28966654e-01, -4.96956389e-02,  8.93371429e-01, ...,\n",
       "          1.09705926e+00,  1.00635301e+00,  1.02800172e+00],\n",
       "...\n",
       "        [-2.84305587e-01, -6.70385111e-01,  8.64294119e-01, ...,\n",
       "          7.87187336e-01, -4.05902351e-01, -7.30015634e-01],\n",
       "        [ 5.72927977e-01,  7.99807598e-01, -5.61028514e-01, ...,\n",
       "          8.49511565e-01,  7.24368447e-01,  9.59548004e-01],\n",
       "        [-3.43706430e-02, -2.13089796e-01,  9.12305310e-01, ...,\n",
       "          9.38030275e-01,  4.80222819e-01,  8.31521531e-01]],\n",
       "\n",
       "       [[ 1.49402712e-01,  1.78027470e-01,  4.31930485e-01, ...,\n",
       "          4.81097061e-01,  5.32524573e-01,  5.49869737e-01],\n",
       "        [ 8.48410536e-01,  1.40244506e-01,  1.08302440e+00, ...,\n",
       "          1.14312696e+00,  6.90461570e-01,  1.01506251e+00],\n",
       "        [-3.26341405e-01, -9.31341013e-01,  1.20129791e+00, ...,\n",
       "          1.05282466e+00,  9.13825958e-01,  9.31066379e-01],\n",
       "        ...,\n",
       "        [ 2.00215705e-01,  5.36591551e-01, -2.70852287e-01, ...,\n",
       "          7.06352253e-01,  5.82176321e-01,  6.83604004e-01],\n",
       "        [ 5.76726184e-01,  6.29912974e-01,  5.34077970e-02, ...,\n",
       "          7.66735584e-01,  7.04236498e-01,  7.48458948e-01],\n",
       "        [ 1.17314220e+00,  6.95096737e-01,  1.06796417e+00, ...,\n",
       "          1.09732698e+00,  3.47445117e-01,  8.21757429e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blooms_std_hat</span></div><div class='xr-var-dims'>(chain, draw, blooms_std_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04633 0.02477 ... 0.3828 0.267</div><input id='attrs-c30857af-b165-4c48-b0d9-9b2f60e9e2ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c30857af-b165-4c48-b0d9-9b2f60e9e2ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-400c998f-35ed-4ef2-8b1a-2dfa268acbdd' class='xr-var-data-in' type='checkbox'><label for='data-400c998f-35ed-4ef2-8b1a-2dfa268acbdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.0463254 ,  0.02476859,  0.37320187, ...,  0.90523212,\n",
       "          0.53011558,  0.09205915],\n",
       "        [ 0.29186008,  0.18885816, -0.06106734, ...,  0.9816119 ,\n",
       "          0.63169854,  0.0632436 ],\n",
       "        [ 0.29798341,  0.284066  ,  0.11397076, ...,  0.76958088,\n",
       "          0.50548862,  0.31404787],\n",
       "        ...,\n",
       "        [ 0.19944635,  0.25161844,  0.0525404 , ...,  0.70112669,\n",
       "          0.54008173,  0.31415059],\n",
       "        [ 0.34089784,  0.12748473, -0.02816593, ...,  0.91616261,\n",
       "          0.53619877,  0.22383521],\n",
       "        [ 0.00732367, -0.06633766,  0.3284374 , ...,  0.86417746,\n",
       "          0.63078456,  0.14567097]],\n",
       "\n",
       "       [[ 0.05561144,  0.23044961,  0.24209029, ...,  0.85643235,\n",
       "          0.49810582,  0.25812187],\n",
       "        [ 0.1150434 ,  0.21979565,  0.37690305, ...,  0.89476205,\n",
       "          0.60947367,  0.22322956],\n",
       "        [ 0.17246855,  0.20896049,  0.26490137, ...,  0.90115122,\n",
       "          0.66142092,  0.37014357],\n",
       "...\n",
       "        [ 0.10007018,  0.49877514,  0.19607989, ...,  0.49758795,\n",
       "          0.42841612, -0.03845415],\n",
       "        [-0.04559483,  0.08430191,  0.1650598 , ...,  0.77091817,\n",
       "          0.61079065,  0.19595267],\n",
       "        [ 0.26111839, -0.06284362,  0.21962989, ...,  0.74521217,\n",
       "          0.57775821,  0.19224851]],\n",
       "\n",
       "       [[ 0.51102297,  0.34607688,  0.15661581, ...,  1.04385769,\n",
       "          0.68044627,  0.12178892],\n",
       "        [ 0.03056122,  0.44743767,  0.25100855, ...,  0.89541569,\n",
       "          0.64525104,  0.40298881],\n",
       "        [ 0.36028514,  0.18434664,  0.27400212, ...,  0.9496615 ,\n",
       "          0.77877545,  0.19376282],\n",
       "        ...,\n",
       "        [ 0.36627742,  0.17552147, -0.09645325, ...,  0.94831458,\n",
       "          0.39805783,  0.48319151],\n",
       "        [ 0.24708457,  0.14138798,  0.05353603, ...,  0.67258316,\n",
       "          0.51990772,  0.11513807],\n",
       "        [ 0.09969633,  0.25706263,  0.39136851, ...,  0.7927707 ,\n",
       "          0.3828408 ,  0.26701288]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2443de8d-934f-4bef-ac35-3a88128b39ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-2443de8d-934f-4bef-ac35-3a88128b39ba' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e65c7dd7-b451-4a46-a5c9-b0796ccc271c' class='xr-index-data-in' type='checkbox'/><label for='index-e65c7dd7-b451-4a46-a5c9-b0796ccc271c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca746dc6-7a57-4a8f-b628-6d7cf34e1e1e' class='xr-index-data-in' type='checkbox'/><label for='index-ca746dc6-7a57-4a8f-b628-6d7cf34e1e1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mu_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cfc98914-6721-41be-a54d-de5717247e7e' class='xr-index-data-in' type='checkbox'/><label for='index-cfc98914-6721-41be-a54d-de5717247e7e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_lik_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b977777b-9044-478b-a674-7a5a42d89e89' class='xr-index-data-in' type='checkbox'/><label for='index-b977777b-9044-478b-a674-7a5a42d89e89' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;log_lik_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>blooms_std_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ddf1c5c9-aacc-4751-aa4e-7f87043219d3' class='xr-index-data-in' type='checkbox'/><label for='index-ddf1c5c9-aacc-4751-aa4e-7f87043219d3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;blooms_std_hat_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0260791c-cda0-4616-9f0c-bdf035d1d127' class='xr-section-summary-in' type='checkbox'  ><label for='section-0260791c-cda0-4616-9f0c-bdf035d1d127' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:34.950864</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/2akx274x</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real beta_ws;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent + beta_ws * water_cent .* shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        beta_ws ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
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       "            \n",
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       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 37.17 37.08 37.86 ... 35.73 36.25\n",
       "    acceptance_rate  (chain, draw) float64 0.9824 0.9818 ... 0.9986 0.9993\n",
       "    step_size        (chain, draw) float64 0.7264 0.7264 ... 0.6313 0.6313\n",
       "    tree_depth       (chain, draw) int64 2 2 3 3 2 2 3 3 2 ... 3 3 2 3 3 2 3 3 3\n",
       "    n_steps          (chain, draw) int64 7 3 7 7 3 3 7 7 3 ... 7 7 3 7 7 3 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 -36.43 -35.68 ... -32.65 -33.83\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:35.000044\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0\n",
       "    num_chains:                 4\n",
       "    num_samples:                1000\n",
       "    num_thin:                   1\n",
       "    num_warmup:                 1000\n",
       "    save_warmup:                0\n",
       "    model_name:                 models/2akx274x\n",
       "    program_code:               \\n    data {\\n        int N;\\n        vector[...\n",
       "    random_seed:                None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-882b3e66-bedd-4940-b399-bdeedd637386' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-882b3e66-bedd-4940-b399-bdeedd637386' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bb30df78-70f1-4833-9d31-04fb164c076a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb30df78-70f1-4833-9d31-04fb164c076a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-385319eb-3067-42a0-8c7a-9e56c8d612c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-385319eb-3067-42a0-8c7a-9e56c8d612c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d264127-fa9b-43fa-a0e8-be441ae26106' class='xr-var-data-in' type='checkbox'><label for='data-3d264127-fa9b-43fa-a0e8-be441ae26106' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-0753afc8-a0a1-45ec-b9cf-88f47481951f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0753afc8-a0a1-45ec-b9cf-88f47481951f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b55ccd71-892a-4913-8d81-db9a91de1244' class='xr-var-data-in' type='checkbox'><label for='data-b55ccd71-892a-4913-8d81-db9a91de1244' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-441dc9ec-fcc2-4812-8965-3e816f30b39a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-441dc9ec-fcc2-4812-8965-3e816f30b39a' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>37.17 37.08 37.86 ... 35.73 36.25</div><input id='attrs-e422ff13-68d6-4254-8d5b-6593a7046571' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e422ff13-68d6-4254-8d5b-6593a7046571' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-839567ff-e8e2-4213-9f75-d2d608920fce' class='xr-var-data-in' type='checkbox'><label for='data-839567ff-e8e2-4213-9f75-d2d608920fce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[37.17476922, 37.08022354, 37.85707771, ..., 38.37988623,\n",
       "        38.37988623, 39.12037867],\n",
       "       [36.56589271, 37.37729947, 35.89476304, ..., 36.90054186,\n",
       "        35.43987565, 35.93778519],\n",
       "       [33.48980878, 33.18306489, 36.08926564, ..., 30.3703066 ,\n",
       "        36.53647616, 37.61992715],\n",
       "       [32.14811601, 38.71834847, 34.12871723, ..., 34.24667719,\n",
       "        35.73270184, 36.25401504]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9824 0.9818 ... 0.9986 0.9993</div><input id='attrs-1c112797-d1e9-4fb1-ab3c-c3c93ed7af36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c112797-d1e9-4fb1-ab3c-c3c93ed7af36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09f38e6c-90d7-4615-8d9e-138bd515efac' class='xr-var-data-in' type='checkbox'><label for='data-09f38e6c-90d7-4615-8d9e-138bd515efac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.98235854, 0.98181903, 0.91394172, ..., 1.        , 0.68418114,\n",
       "        1.        ],\n",
       "       [0.8580094 , 0.97349576, 0.92912702, ..., 0.92227392, 0.93014761,\n",
       "        0.98513292],\n",
       "       [0.99421326, 0.98115494, 0.99593533, ..., 0.88496238, 1.        ,\n",
       "        0.98099848],\n",
       "       [0.99067039, 0.8919415 , 0.62481845, ..., 0.71084799, 0.99855068,\n",
       "        0.9993465 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7264 0.7264 ... 0.6313 0.6313</div><input id='attrs-e693589e-6309-42a6-a56c-75d0e1fa4bf0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e693589e-6309-42a6-a56c-75d0e1fa4bf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ab6da64-c2d1-4d5b-ae29-a5c8f7552e52' class='xr-var-data-in' type='checkbox'><label for='data-5ab6da64-c2d1-4d5b-ae29-a5c8f7552e52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.72643421, 0.72643421, 0.72643421, ..., 0.72643421, 0.72643421,\n",
       "        0.72643421],\n",
       "       [0.56420716, 0.56420716, 0.56420716, ..., 0.56420716, 0.56420716,\n",
       "        0.56420716],\n",
       "       [0.54673893, 0.54673893, 0.54673893, ..., 0.54673893, 0.54673893,\n",
       "        0.54673893],\n",
       "       [0.63127613, 0.63127613, 0.63127613, ..., 0.63127613, 0.63127613,\n",
       "        0.63127613]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 3 3 2 2 3 3 ... 3 2 3 3 2 3 3 3</div><input id='attrs-99074329-22a5-4436-9153-b16f4ca81a46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99074329-22a5-4436-9153-b16f4ca81a46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab2d1033-9bf3-441f-8f59-648439a640e2' class='xr-var-data-in' type='checkbox'><label for='data-ab2d1033-9bf3-441f-8f59-648439a640e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 3, ..., 2, 2, 3],\n",
       "       [3, 3, 2, ..., 3, 3, 3],\n",
       "       [3, 2, 3, ..., 2, 3, 3],\n",
       "       [2, 3, 3, ..., 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>7 3 7 7 3 3 7 7 ... 7 3 7 7 3 7 7 7</div><input id='attrs-da3ec069-4454-436b-ac7d-165255649594' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da3ec069-4454-436b-ac7d-165255649594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36073907-2dab-4019-befd-acae08cc72d5' class='xr-var-data-in' type='checkbox'><label for='data-36073907-2dab-4019-befd-acae08cc72d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7, 3, 7, ..., 3, 3, 7],\n",
       "       [7, 7, 3, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 3, 7, 7],\n",
       "       [3, 7, 7, ..., 7, 7, 7]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-8d9daf3c-8f13-46de-849f-6e2b9ea33270' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d9daf3c-8f13-46de-849f-6e2b9ea33270' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a68fdf8-d6d3-48a9-ae58-4f7a5dee5ed1' class='xr-var-data-in' type='checkbox'><label for='data-4a68fdf8-d6d3-48a9-ae58-4f7a5dee5ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-36.43 -35.68 ... -32.65 -33.83</div><input id='attrs-7325118d-8051-4711-8cf4-f87e93645ce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7325118d-8051-4711-8cf4-f87e93645ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47e047f7-7e33-4013-8102-c2e2da319a64' class='xr-var-data-in' type='checkbox'><label for='data-47e047f7-7e33-4013-8102-c2e2da319a64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-36.43421644, -35.68436731, -34.09173843, ..., -35.79997085,\n",
       "        -35.47138861, -37.98232571],\n",
       "       [-34.01217835, -34.31608931, -35.5817405 , ..., -33.76478259,\n",
       "        -33.40508759, -33.43048778],\n",
       "       [-32.53738813, -32.27646822, -30.65757325, ..., -29.57387547,\n",
       "        -29.37625166, -33.91819817],\n",
       "       [-31.95874109, -28.00690724, -32.28058677, ..., -30.87933265,\n",
       "        -32.64867508, -33.82932667]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d940af4b-281b-4bc3-b1f7-160959a86435' class='xr-section-summary-in' type='checkbox'  ><label for='section-d940af4b-281b-4bc3-b1f7-160959a86435' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-98d904af-789f-4a17-a427-8119ffb38a40' class='xr-index-data-in' type='checkbox'/><label for='index-98d904af-789f-4a17-a427-8119ffb38a40' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02cc59d5-505f-4d2d-9e61-dda6116955ef' class='xr-index-data-in' type='checkbox'/><label for='index-02cc59d5-505f-4d2d-9e61-dda6116955ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd59c317-decd-4895-90cc-c52ad5a50178' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd59c317-decd-4895-90cc-c52ad5a50178' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:35.000044</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd><dt><span>num_chains :</span></dt><dd>4</dd><dt><span>num_samples :</span></dt><dd>1000</dd><dt><span>num_thin :</span></dt><dd>1</dd><dt><span>num_warmup :</span></dt><dd>1000</dd><dt><span>save_warmup :</span></dt><dd>0</dd><dt><span>model_name :</span></dt><dd>models/2akx274x</dd><dt><span>program_code :</span></dt><dd>\n",
       "    data {\n",
       "        int N;\n",
       "        vector[N] blooms_std;\n",
       "        vector[N] water_cent;\n",
       "        vector[N] shade_cent;\n",
       "    }\n",
       "    \n",
       "    parameters {\n",
       "        real alpha;\n",
       "        real beta_w;\n",
       "        real beta_s;\n",
       "        real beta_ws;\n",
       "        real&lt;lower=0&gt; sigma;\n",
       "    }\n",
       "    \n",
       "    transformed parameters {\n",
       "        vector[N] mu;\n",
       "        \n",
       "        mu = alpha + beta_w * water_cent + beta_s * shade_cent + beta_ws * water_cent .* shade_cent;\n",
       "    }\n",
       "    \n",
       "    model {\n",
       "        // Priori\n",
       "        \n",
       "        alpha ~ normal(0.5, 0.25);\n",
       "        beta_w ~ normal(0, 0.25);\n",
       "        beta_s ~ normal(0, 0.25);\n",
       "        beta_ws ~ normal(0, 0.25);\n",
       "        sigma ~ exponential(1);\n",
       "        \n",
       "        blooms_std ~ normal(mu, sigma);\n",
       "    }\n",
       "    \n",
       "    generated quantities {\n",
       "        vector[N] log_lik;\n",
       "        vector[N] blooms_std_hat;\n",
       "        \n",
       "        for(i in 1:N){\n",
       "            log_lik[i] = normal_lpdf(blooms_std[i] | mu[i], sigma);\n",
       "            blooms_std_hat[i] = normal_rng(mu[i], sigma);\n",
       "        }\n",
       "    }\n",
       "</dd><dt><span>random_seed :</span></dt><dd>None</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (blooms_std_dim_0: 27, water_cent_dim_0: 27,\n",
       "                       shade_cent_dim_0: 27)\n",
       "Coordinates:\n",
       "  * blooms_std_dim_0  (blooms_std_dim_0) int64 0 1 2 3 4 5 ... 21 22 23 24 25 26\n",
       "  * water_cent_dim_0  (water_cent_dim_0) int64 0 1 2 3 4 5 ... 21 22 23 24 25 26\n",
       "  * shade_cent_dim_0  (shade_cent_dim_0) int64 0 1 2 3 4 5 ... 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    blooms_std        (blooms_std_dim_0) float64 0.0 0.0 0.307 ... 0.6894 0.3721\n",
       "    water_cent        (water_cent_dim_0) float64 -1.0 -1.0 -1.0 ... 1.0 1.0 1.0\n",
       "    shade_cent        (shade_cent_dim_0) float64 -1.0 0.0 1.0 ... -1.0 0.0 1.0\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:56:34.723805\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          stan\n",
       "    inference_library_version:  3.7.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f4b11dff-bb94-4432-9c2b-22f379176bb6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4b11dff-bb94-4432-9c2b-22f379176bb6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>blooms_std_dim_0</span>: 27</li><li><span class='xr-has-index'>water_cent_dim_0</span>: 27</li><li><span class='xr-has-index'>shade_cent_dim_0</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a880f855-6a14-4cf0-93c6-38e311ce1ece' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a880f855-6a14-4cf0-93c6-38e311ce1ece' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>blooms_std_dim_0</span></div><div class='xr-var-dims'>(blooms_std_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-88a6a966-3f17-482c-8e6e-fb2812537940' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-88a6a966-3f17-482c-8e6e-fb2812537940' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68822607-8558-44d0-8883-ae3665204e8d' class='xr-var-data-in' type='checkbox'><label for='data-68822607-8558-44d0-8883-ae3665204e8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>water_cent_dim_0</span></div><div class='xr-var-dims'>(water_cent_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-c280a309-e00b-42da-aa95-819e3cec2fdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c280a309-e00b-42da-aa95-819e3cec2fdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04d42e82-9d87-40dc-afa7-d10d6a2f0a62' class='xr-var-data-in' type='checkbox'><label for='data-04d42e82-9d87-40dc-afa7-d10d6a2f0a62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>shade_cent_dim_0</span></div><div class='xr-var-dims'>(shade_cent_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-6c92e90a-acff-4758-800d-e4d7e36633b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c92e90a-acff-4758-800d-e4d7e36633b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5f125fe-cecb-4239-8353-71c84a5083c6' class='xr-var-data-in' type='checkbox'><label for='data-d5f125fe-cecb-4239-8353-71c84a5083c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-883f2c66-3a7f-4447-b327-c8fe7afa5d29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-883f2c66-3a7f-4447-b327-c8fe7afa5d29' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blooms_std</span></div><div class='xr-var-dims'>(blooms_std_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.307 ... 0.6894 0.3721</div><input id='attrs-4d620306-c4c6-477d-b295-8ce2fbf7552d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d620306-c4c6-477d-b295-8ce2fbf7552d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3acac389-b342-4a62-b111-30dab2a23798' class='xr-var-data-in' type='checkbox'><label for='data-3acac389-b342-4a62-b111-30dab2a23798' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.        , 0.        , 0.3070287 , 0.50729967, 0.16357905,\n",
       "       0.21221589, 0.62204833, 0.23162639, 0.37314052, 0.22147874,\n",
       "       0.23765415, 0.0549411 , 0.5893104 , 0.34560084, 0.18105403,\n",
       "       1.        , 0.54506996, 0.37308522, 0.02770558, 0.13186418,\n",
       "       0.29516673, 0.68019687, 0.3758226 , 0.25067743, 0.8420063 ,\n",
       "       0.68940441, 0.37214511])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_cent</span></div><div class='xr-var-dims'>(water_cent_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -1.0 -1.0 0.0 ... 1.0 1.0 1.0</div><input id='attrs-0ae1ed32-451a-4d92-b270-98648566d51c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ae1ed32-451a-4d92-b270-98648566d51c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b69c38d2-499d-4673-b430-ec4a2575fd5d' class='xr-var-data-in' type='checkbox'><label for='data-b69c38d2-499d-4673-b430-ec4a2575fd5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1., -1., -1.,  0.,  0.,  0.,  1.,  1.,  1., -1., -1., -1.,  0.,\n",
       "        0.,  0.,  1.,  1.,  1., -1., -1., -1.,  0.,  0.,  0.,  1.,  1.,\n",
       "        1.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shade_cent</span></div><div class='xr-var-dims'>(shade_cent_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 0.0 1.0 -1.0 ... -1.0 0.0 1.0</div><input id='attrs-0ca9d5a8-f703-4ffd-af60-594f408d23ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ca9d5a8-f703-4ffd-af60-594f408d23ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6951919d-883d-4330-afb5-a1a292edb378' class='xr-var-data-in' type='checkbox'><label for='data-6951919d-883d-4330-afb5-a1a292edb378' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1.,  0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,\n",
       "        0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,  0.,  1., -1.,  0.,\n",
       "        1.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6013685d-60d3-4e26-8d7e-8f0d9780f1f2' class='xr-section-summary-in' type='checkbox'  ><label for='section-6013685d-60d3-4e26-8d7e-8f0d9780f1f2' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>blooms_std_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c2cac39-d55a-4283-b704-b5cdf489ab26' class='xr-index-data-in' type='checkbox'/><label for='index-9c2cac39-d55a-4283-b704-b5cdf489ab26' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;blooms_std_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>water_cent_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5ea0ca7-c520-4e1c-80e4-13093b3e1cda' class='xr-index-data-in' type='checkbox'/><label for='index-d5ea0ca7-c520-4e1c-80e4-13093b3e1cda' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;water_cent_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>shade_cent_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe48c89b-df7c-4c30-8652-b13bf5174160' class='xr-index-data-in' type='checkbox'/><label for='index-fe48c89b-df7c-4c30-8652-b13bf5174160' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;shade_cent_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2389c597-bff6-45b9-a16b-928a10088fe9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2389c597-bff6-45b9-a16b-928a10088fe9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-08-11T18:56:34.723805</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>stan</dd><dt><span>inference_library_version :</span></dt><dd>3.7.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
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       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
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       "  padding-bottom: 6px;\n",
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       "\n",
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       ".xr-header > ul {\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-dim-list {\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-list,\n",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> sample_stats_prior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stan_blooms_interaction = az.from_pystan(\n",
    "    prior_model=posteriori,\n",
    "    prior=samples,\n",
    "    posterior_model=posteriori,\n",
    "    posterior=samples,\n",
    "    posterior_predictive=\"blooms_std_hat\",\n",
    "    observed_data=['blooms_std', 'water_cent', 'shade_cent'],\n",
    ")\n",
    "\n",
    "stan_blooms_interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7b1fc301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.358</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5629.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_w</th>\n",
       "      <td>0.206</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4954.0</td>\n",
       "      <td>2852.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_s</th>\n",
       "      <td>-0.113</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.177</td>\n",
       "      <td>-0.051</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5101.0</td>\n",
       "      <td>3033.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_ws</th>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.042</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5011.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha    0.358  0.029   0.301    0.411      0.000      0.0    5629.0   \n",
       "beta_w   0.206  0.034   0.145    0.275      0.000      0.0    4954.0   \n",
       "beta_s  -0.113  0.034  -0.177   -0.051      0.000      0.0    5101.0   \n",
       "beta_ws -0.143  0.042  -0.220   -0.059      0.001      0.0    5011.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "alpha      2730.0    1.0  \n",
       "beta_w     2852.0    1.0  \n",
       "beta_s     3033.0    1.0  \n",
       "beta_ws    3012.0    1.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(stan_blooms_interaction, var_names=['alpha', 'beta_w', 'beta_s', 'beta_ws'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f8f27b5",
   "metadata": {},
   "source": [
    "### R Code 8.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d01a850d",
   "metadata": {},
   "outputs": [],
   "source": [
    "blooms_post = az.extract(stan_blooms.posterior, num_samples=20)  # get 20 lines\n",
    "blooms_post_int = az.extract(stan_blooms_interaction.posterior, num_samples=20)  # get 20 lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bf792111",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1224x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ================\n",
    "# Plot without interactions\n",
    "\n",
    "water_seq = np.linspace(-1, 1, 30)\n",
    "\n",
    "fig = plt.figure(figsize=(17, 6))\n",
    "\n",
    "gs = GridSpec(1, 3)\n",
    "\n",
    "shade_cent_values = [-1, 0, 1]\n",
    "\n",
    "ax = [None] * len(shade_cent_values)\n",
    "\n",
    "for j, shade_plot_aux in enumerate(shade_cent_values):\n",
    "    ax[j] = fig.add_subplot(gs[j])\n",
    "\n",
    "    for i in range(len(blooms_post.alpha)):\n",
    "        mu = blooms_post.alpha[i].values + \\\n",
    "             blooms_post.beta_w[i].values * water_seq + \\\n",
    "             blooms_post.beta_s[i].values * (shade_plot_aux)\n",
    "\n",
    "        ax[j].plot(water_seq, mu, c='gray')\n",
    "        ax[j].set_ylim(0, 1)\n",
    "        ax[j].set_title(f'8.4 post: Shade = ${shade_plot_aux}$')\n",
    "        ax[j].set_xlabel('water')\n",
    "        ax[j].set_ylabel('blooms')\n",
    "        ax[j].set_xticks(shade_cent_values, shade_cent_values)\n",
    "        ax[j].set_yticks([0, 0.5, 1.0], [0, 0.5, 1])\n",
    "\n",
    "    ax[j].plot(\n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'water_cent'].values, \n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'blooms_std'].values, \n",
    "            'o', c='blue')\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "# ================\n",
    "# Plot with interactions\n",
    "\n",
    "water_seq = np.linspace(-1, 1, 30)\n",
    "\n",
    "fig = plt.figure(figsize=(17, 6))\n",
    "\n",
    "gs = GridSpec(1, 3)\n",
    "\n",
    "shade_cent_values = [-1, 0, 1]\n",
    "\n",
    "ax = [None] * len(shade_cent_values)\n",
    "\n",
    "for j, shade_plot_aux in enumerate(shade_cent_values):\n",
    "    ax[j] = fig.add_subplot(gs[j])\n",
    "\n",
    "    for i in range(len(blooms_post_int.alpha)):\n",
    "        mu = blooms_post_int.alpha[i].values + \\\n",
    "             blooms_post_int.beta_w[i].values * water_seq + \\\n",
    "             blooms_post_int.beta_s[i].values * (shade_plot_aux) + \\\n",
    "             blooms_post_int.beta_ws[i].values * water_seq * (shade_plot_aux)\n",
    "\n",
    "        ax[j].plot(water_seq, mu, c='gray')\n",
    "        ax[j].set_ylim(0, 1)\n",
    "        ax[j].set_title(f'8.5 post: Shade = ${shade_plot_aux}$')\n",
    "        ax[j].set_xlabel('water')\n",
    "        ax[j].set_ylabel('blooms')\n",
    "        ax[j].set_xticks(shade_cent_values, shade_cent_values)\n",
    "        ax[j].set_yticks([0, 0.5, 1.0], [0, 0.5, 1])\n",
    "\n",
    "    ax[j].plot(\n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'water_cent'].values, \n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'blooms_std'].values, \n",
    "            'o', c='blue')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d679e1c",
   "metadata": {},
   "source": [
    "### R Code 8.26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ca419ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ================\n",
    "# Plot without interactions\n",
    "\n",
    "# Prior\n",
    "alpha_prior =  np.random.normal(0.5, 0.25, 20)\n",
    "beta_w_prior =  np.random.normal(0, 0.25, 20)\n",
    "beta_s_prior =  np.random.normal(0, 0.25, 20)\n",
    "\n",
    "water_seq = np.linspace(-1, 1, 30)\n",
    "\n",
    "fig = plt.figure(figsize=(17, 6))\n",
    "\n",
    "gs = GridSpec(1, 3)\n",
    "\n",
    "shade_cent_values = [-1, 0, 1]\n",
    "\n",
    "ax = [None] * len(shade_cent_values)\n",
    "\n",
    "for j, shade_plot_aux in enumerate(shade_cent_values):\n",
    "    ax[j] = fig.add_subplot(gs[j])\n",
    "\n",
    "    for i in range(len(alpha_prior)):\n",
    "        mu = alpha_prior[i] + \\\n",
    "             beta_w_prior[i] * water_seq + \\\n",
    "             beta_s_prior[i] * (shade_plot_aux)\n",
    "\n",
    "        ax[j].plot(water_seq, mu, c='gray')\n",
    "        ax[j].set_ylim(0, 1)\n",
    "        ax[j].set_title(f'8.4 prior: Shade = ${shade_plot_aux}$')\n",
    "        ax[j].set_xlabel('water')\n",
    "        ax[j].set_ylabel('blooms')\n",
    "        ax[j].set_xticks(shade_cent_values, shade_cent_values)\n",
    "        ax[j].set_yticks([0, 0.5, 1.0], [0, 0.5, 1])\n",
    "        ax[j].set_ylim(-1, 2)\n",
    "        ax[j].axhline(y=1, ls='--', color='red')\n",
    "        ax[j].axhline(y=0, ls='--', color='red')\n",
    "\n",
    "    ax[j].plot(\n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'water_cent'].values, \n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'blooms_std'].values, \n",
    "            'o', c='blue')\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "# ================\n",
    "# Plot with interactions\n",
    "\n",
    "# Prior\n",
    "alpha_prior =  np.random.normal(0.5, 0.25, 20)\n",
    "beta_w_prior =  np.random.normal(0, 0.25, 20)\n",
    "beta_s_prior =  np.random.normal(0, 0.25, 20)\n",
    "beta_ws_prior =  np.random.normal(0, 0.25, 20)\n",
    "\n",
    "water_seq = np.linspace(-1, 1, 30)\n",
    "\n",
    "fig = plt.figure(figsize=(17, 6))\n",
    "\n",
    "gs = GridSpec(1, 3)\n",
    "\n",
    "shade_cent_values = [-1, 0, 1]\n",
    "\n",
    "ax = [None] * len(shade_cent_values)\n",
    "\n",
    "for j, shade_plot_aux in enumerate(shade_cent_values):\n",
    "    ax[j] = fig.add_subplot(gs[j])\n",
    "\n",
    "    for i in range(len(alpha_prior)):\n",
    "        mu = alpha_prior[i] + \\\n",
    "             beta_w_prior[i] * water_seq + \\\n",
    "             beta_s_prior[i] * (shade_plot_aux) + \\\n",
    "             beta_ws_prior[i] * water_seq * (shade_plot_aux)\n",
    "\n",
    "        ax[j].plot(water_seq, mu, c='gray')\n",
    "        ax[j].set_ylim(0, 1)\n",
    "        ax[j].set_title(f'8.5 post: Shade = ${shade_plot_aux}$')\n",
    "        ax[j].set_xlabel('water')\n",
    "        ax[j].set_ylabel('blooms')\n",
    "        ax[j].set_xticks(shade_cent_values, shade_cent_values)\n",
    "        ax[j].set_yticks([0, 0.5, 1.0], [0, 0.5, 1])\n",
    "        ax[j].set_ylim(-1, 2)\n",
    "        ax[j].axhline(y=1, ls='--', color='red')\n",
    "        ax[j].axhline(y=0, ls='--', color='red')\n",
    "\n",
    "    ax[j].plot(\n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'water_cent'].values, \n",
    "            df.loc[df['shade_cent'] == shade_plot_aux,'blooms_std'].values, \n",
    "            'o', c='blue')\n",
    "    \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}