{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95177c93",
   "metadata": {},
   "source": [
    "# 7 - Compasso de Ulisses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c959eff7",
   "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",
    "from videpy import Vide\n",
    "\n",
    "import networkx as nx\n",
    "# from causalgraphicalmodels import CausalGraphicalModel\n",
    "\n",
    "import stan\n",
    "import nest_asyncio\n",
    "\n",
    "plt.style.use('default')\n",
    "\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": "d78c9936",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To running the stan in jupyter notebook\n",
    "nest_asyncio.apply()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387751b9",
   "metadata": {},
   "source": [
    "### R Code 7.1 - Pag 194"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3b2396b",
   "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>species</th>\n",
       "      <th>brain</th>\n",
       "      <th>mass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>afarensis</td>\n",
       "      <td>438</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>africanus</td>\n",
       "      <td>452</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>habilis</td>\n",
       "      <td>612</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boisei</td>\n",
       "      <td>521</td>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rudolfensis</td>\n",
       "      <td>752</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ergaster</td>\n",
       "      <td>871</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sapiens</td>\n",
       "      <td>1350</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       species  brain  mass\n",
       "0    afarensis    438  37.0\n",
       "1    africanus    452  35.5\n",
       "2      habilis    612  34.5\n",
       "3       boisei    521  41.5\n",
       "4  rudolfensis    752  55.5\n",
       "5     ergaster    871  61.0\n",
       "6      sapiens   1350  53.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sppnames = ('afarensis', 'africanus', 'habilis',  'boisei', 'rudolfensis', 'ergaster', 'sapiens')\n",
    "brainvolcc = (438, 452, 612, 521,  752, 871, 1350)\n",
    "masskg = (37.0, 35.5, 34.5, 41.5, 55.5, 61.0, 53.5)\n",
    "\n",
    "d = pd.DataFrame({'species':sppnames, 'brain': brainvolcc, 'mass':masskg})\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f9721d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "ax.scatter(d.mass, d.brain, marker='o', s=50)\n",
    "\n",
    "for i, text in enumerate(sppnames):\n",
    "    ax.annotate(text, (d.mass[i]+0.3, d.brain[i]+10))\n",
    "    \n",
    "ax.set_title('Figure 7.2 - Relationshiip between Brain and Body hominin species')\n",
    "ax.set_xlabel('Body mass')\n",
    "ax.set_ylabel('Brain volume')\n",
    "\n",
    "ax.grid(ls='--', color='white', alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d1d8f5e",
   "metadata": {},
   "source": [
    "### R Code 7.2 - pag 196"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0829d158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.90489186863706"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(d.mass, ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9494969c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.90489186863706"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.mass.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb45ab23",
   "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>species</th>\n",
       "      <th>brain</th>\n",
       "      <th>mass</th>\n",
       "      <th>mass_std</th>\n",
       "      <th>brain_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>afarensis</td>\n",
       "      <td>438</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.779467</td>\n",
       "      <td>0.324444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>africanus</td>\n",
       "      <td>452</td>\n",
       "      <td>35.5</td>\n",
       "      <td>-0.917020</td>\n",
       "      <td>0.334815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>habilis</td>\n",
       "      <td>612</td>\n",
       "      <td>34.5</td>\n",
       "      <td>-1.008722</td>\n",
       "      <td>0.453333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boisei</td>\n",
       "      <td>521</td>\n",
       "      <td>41.5</td>\n",
       "      <td>-0.366808</td>\n",
       "      <td>0.385926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rudolfensis</td>\n",
       "      <td>752</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0.917020</td>\n",
       "      <td>0.557037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ergaster</td>\n",
       "      <td>871</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.421380</td>\n",
       "      <td>0.645185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sapiens</td>\n",
       "      <td>1350</td>\n",
       "      <td>53.5</td>\n",
       "      <td>0.733616</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       species  brain  mass  mass_std  brain_std\n",
       "0    afarensis    438  37.0 -0.779467   0.324444\n",
       "1    africanus    452  35.5 -0.917020   0.334815\n",
       "2      habilis    612  34.5 -1.008722   0.453333\n",
       "3       boisei    521  41.5 -0.366808   0.385926\n",
       "4  rudolfensis    752  55.5  0.917020   0.557037\n",
       "5     ergaster    871  61.0  1.421380   0.645185\n",
       "6      sapiens   1350  53.5  0.733616   1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['mass_std'] = (d.mass - d.mass.mean())/d.mass.std()\n",
    "d['brain_std'] = d.brain / np.max(d.brain)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bcc0daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "ax.scatter(d.mass_std, d.brain_std, marker='o', s=50)\n",
    "\n",
    "for i, text in enumerate(sppnames):\n",
    "    ax.annotate(text, (d.mass_std[i]+0.03, d.brain_std[i]+0.03))\n",
    "    \n",
    "ax.set_title('Figure 7.2 - Relationshiip between Brain and Body hominin species')\n",
    "ax.set_xlabel('Body mass - Rescaling')\n",
    "ax.set_ylabel('Brain volume - Rescaling')\n",
    "\n",
    "ax.grid(ls='--', color='white', alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d79ddc",
   "metadata": {},
   "source": [
    "### R Code 7.3 - pag 196"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120223ca",
   "metadata": {},
   "source": [
    "Modelo linear:\n",
    "\n",
    "$\\begin{align}\n",
    "b_i \\sim Normal(\\mu_i, \\sigma) \\\\ \n",
    "\\mu_i = \\alpha + \\beta m_i \\\\\n",
    "\\alpha \\sim Normal(0.5, 1) \\\\\n",
    "\\beta \\sim Normal(0, 10) \\\\\n",
    "\\sigma \\sim LogNormal(0, 1) \\\\\n",
    "\\end{align}$\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6b97033",
   "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 2.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.21 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",
      "  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",
      "  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"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta;\n",
    "        real<lower=0> sigma;\n",
    "        // real log_sigma;  // Like the book\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta * body;\n",
    "                \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        beta ~ normal(0, 10);\n",
    "        sigma ~ lognormal(0, 1);\n",
    "        // log_sigma ~ normal(0, 1);  // Like the book \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "        // brain ~ normal(mu, exp(sigma));  // Like the book\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_1 = stan.build(model, data=data)\n",
    "samples_1 = posteriori_1.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f59bd82e",
   "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>std</th>\n",
       "      <th>7.0%</th>\n",
       "      <th>93.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean   std  7.0%  93.0%\n",
       "alpha  0.53  0.11  0.31   0.71\n",
       "beta   0.16  0.12 -0.06   0.37\n",
       "sigma  0.27  0.12  0.12   0.46"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vide.summary(samples_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dff4aa15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Vide.plot_forest(samples_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1285624b",
   "metadata": {},
   "source": [
    "### R Code 7.4 - pag 196\n",
    "\n",
    "Just example code in R\n",
    "\n",
    "`m7.1_OLS <- lm(brain_std ~ brain_std, data=d)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c5a8ca",
   "metadata": {},
   "source": [
    "### R Code 7.5 - pag 197"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a43935e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def var2(x):\n",
    "    return np.sum(np.power(x - np.mean(x), 2))/len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f46b43da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4887460670115684"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_std = [np.mean((samples_1['alpha'].flatten() + samples_1['beta'].flatten() * mass)) for mass in d.mass_std.values]\n",
    "r = mean_std - d.brain_std\n",
    "\n",
    "resid_var = var2(r)\n",
    "outcome_var = var2(d.brain_std)\n",
    "\n",
    "1 - resid_var/outcome_var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3229e7a8",
   "metadata": {},
   "source": [
    "### R Code 7.6 - Pag 197"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95ae50be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def R2_is_bad():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3bd17c",
   "metadata": {},
   "source": [
    "### R Code 7.7 - pag 198"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a03f5b6f",
   "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_brnqnd8l/model_5rhounxn.stan', line 10, 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 1.5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 3.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.37 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"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[2];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body + beta[2] * body^2;\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:2){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 1);    \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_2 = stan.build(model, data=data)\n",
    "samples_2 = posteriori_2.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e15e5ab",
   "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>std</th>\n",
       "      <th>7.0%</th>\n",
       "      <th>93.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[0]</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean   std  7.0%  93.0%\n",
       "alpha    0.58  0.25  0.06   1.00\n",
       "beta[0]  0.18  0.17 -0.13   0.47\n",
       "beta[1] -0.07  0.26 -0.52   0.43\n",
       "sigma    0.31  0.16  0.12   0.59"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vide.summary(samples_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "96632604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Vide.plot_forest(samples_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e8a2a7",
   "metadata": {},
   "source": [
    "### R Code 7.8 - Pag 198"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed397127",
   "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_7jl6mmu9/model_w6rff3bk.stan', line 10, 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 1.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.21 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] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[3];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3;\n",
    "                     \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:3){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 1);    \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_3 = stan.build(model, data=data)\n",
    "samples_3 = posteriori_3.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97a7c9fe",
   "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_89iqtyto/model_5n3a3kju.stan', line 10, 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  10% (800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  35% (2800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  60% (4800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  85% (6800/8000)\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 1.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.17 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 1.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[4];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3 +\n",
    "                     beta[4] * body^4;\n",
    "                     \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:4){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 1);    \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_4 = stan.build(model, data=data)\n",
    "samples_4 = posteriori_4.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "890ea7f3",
   "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_sc94oycl/model_3c7wjchh.stan', line 10, 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   2% (200/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   6% (500/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  11% (900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  16% (1300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  39% (3100/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  60% (4800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  80% (6400/8000)\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 3.1e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[5];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3 +\n",
    "                     beta[4] * body^4 +\n",
    "                     beta[5] * body^5;\n",
    "                     \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:5){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 1);    \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_5 = stan.build(model, data=data)\n",
    "samples_5 = posteriori_5.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0465db4",
   "metadata": {},
   "source": [
    "### R Code 7.9 - Pag 199"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7550eaa",
   "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_bic3rat1/model_3skjj3wc.stan', line 10, 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_bic3rat1/model_3skjj3wc.stan', line 24, column 27: Argument\n",
      "    0.001 suggests there may be parameters that are not unit scale; consider\n",
      "    rescaling with a multiplier (see manual section 22.12).\n",
      "\u001b[36mSampling:\u001b[0m   0%\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   4% (300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m   6% (500/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  11% (900/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  18% (1400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  20% (1600/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  22% (1800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  25% (2000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  28% (2200/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  30% (2400/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  32% (2600/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  35% (2800/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  38% (3000/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  54% (4300/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  71% (5700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  86% (6900/8000)\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 2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.\n",
      "  Adjust your expectations accordingly!\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 1.9e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[6];\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3 +\n",
    "                     beta[4] * body^4 +\n",
    "                     beta[5] * body^5 +\n",
    "                     beta[6] * body^6;\n",
    "                     \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, 0.001);\n",
    "        \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:6){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }    \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_6 = stan.build(model, data=data)\n",
    "samples_6 = posteriori_6.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf93c119",
   "metadata": {},
   "source": [
    "### R Code 7.10 - Pag 199"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8e7d1de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mass_seq = np.linspace(start=d.mass_std.min(), stop=d.mass_std.max(), num=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d119d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_1 = [samples_1['alpha'].flatten() + samples_1['beta'].flatten() * mass_i for mass_i in mass_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd1ac7fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_2 = [samples_2['alpha'].flatten() + \n",
    "        samples_2['beta'][0].flatten() * mass_i + \n",
    "        samples_2['beta'][1].flatten() * np.power(mass_i, 2) for mass_i in mass_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "730b207a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_3 = [samples_3['alpha'].flatten() + \n",
    "        samples_3['beta'][0].flatten() * mass_i + \n",
    "        samples_3['beta'][1].flatten() * np.power(mass_i, 2) + \n",
    "        samples_3['beta'][2].flatten() * np.power(mass_i, 3)\n",
    "        for mass_i in mass_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5603d91e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_4 = [samples_4['alpha'].flatten() + \n",
    "        samples_4['beta'][0].flatten() * mass_i + \n",
    "        samples_4['beta'][1].flatten() * np.power(mass_i, 2) + \n",
    "        samples_4['beta'][2].flatten() * np.power(mass_i, 3) +\n",
    "        samples_4['beta'][3].flatten() * np.power(mass_i, 4) \n",
    "        for mass_i in mass_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a46ac37e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_5 = [samples_5['alpha'].flatten() + \n",
    "        samples_5['beta'][0].flatten() * mass_i + \n",
    "        samples_5['beta'][1].flatten() * np.power(mass_i, 2) + \n",
    "        samples_5['beta'][2].flatten() * np.power(mass_i, 3) +\n",
    "        samples_5['beta'][3].flatten() * np.power(mass_i, 4) + \n",
    "        samples_5['beta'][4].flatten() * np.power(mass_i, 5) \n",
    "        for mass_i in mass_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9eab8d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp_6 = [samples_6['alpha'].flatten() + \n",
    "        samples_6['beta'][0].flatten() * mass_i + \n",
    "        samples_6['beta'][1].flatten() * np.power(mass_i, 2) + \n",
    "        samples_6['beta'][2].flatten() * np.power(mass_i, 3) +\n",
    "        samples_6['beta'][3].flatten() * np.power(mass_i, 4) + \n",
    "        samples_6['beta'][4].flatten() * np.power(mass_i, 5) + \n",
    "        samples_6['beta'][5].flatten() * np.power(mass_i, 6) \n",
    "        for mass_i in mass_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "97da481b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 20))\n",
    "\n",
    "gs = GridSpec(nrows=3, ncols=2)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax1.scatter(d.mass_std.values, d.brain_std.values, c='black')\n",
    "ax1.plot(mass_seq, pp_1, c='blue', lw=0.01)\n",
    "ax1.set_ylim(0, 1.1)\n",
    "ax1.set_title('$R^2: 0.51$')\n",
    "ax1.set_xlabel('Body mass (KG)')\n",
    "ax1.set_ylabel('Brain Volume (cc)')\n",
    "\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "ax2.scatter(d.mass_std.values, d.brain_std.values, c='black')\n",
    "ax2.plot(mass_seq, pp_2, c='blue', lw=0.01)\n",
    "ax2.set_ylim(0, 1.1)\n",
    "ax2.set_title('$R^2: 0.54$')\n",
    "ax2.set_xlabel('Body mass (KG)')\n",
    "ax2.set_ylabel('Brain Volume (cc)')\n",
    "\n",
    "ax3 = fig.add_subplot(gs[1, 0])\n",
    "ax3.scatter(d.mass_std.values, d.brain_std.values, c='black')\n",
    "ax3.plot(mass_seq, pp_3, c='blue', lw=0.01)\n",
    "ax3.set_ylim(0, 1.1)\n",
    "ax3.set_title('$R^2: 0.69$')\n",
    "ax3.set_xlabel('Body mass (KG)')\n",
    "ax3.set_ylabel('Brain Volume (cc)')\n",
    "\n",
    "ax4 = fig.add_subplot(gs[1, 1])\n",
    "ax4.scatter(d.mass_std.values, d.brain_std.values, c='black')\n",
    "ax4.plot(mass_seq, pp_4, c='blue', lw=0.01)\n",
    "ax4.set_ylim(0, 1.1)\n",
    "ax4.set_title('$R^2: 0.82$')\n",
    "ax4.set_xlabel('Body mass (KG)')\n",
    "ax4.set_ylabel('Brain Volume (cc)')\n",
    "\n",
    "ax5 = fig.add_subplot(gs[2, 0])\n",
    "ax5.scatter(d.mass_std.values, d.brain_std.values, c='black')\n",
    "ax5.plot(mass_seq, pp_5, c='blue', lw=0.01)\n",
    "ax5.set_ylim(0, 1.1)\n",
    "ax5.set_title('$R^2: 0.99$')\n",
    "ax5.set_xlabel('Body mass (KG)')\n",
    "ax5.set_ylabel('Brain Volume (cc)')\n",
    "\n",
    "ax6 = fig.add_subplot(gs[2, 1])\n",
    "ax6.scatter(d.mass_std.values, d.brain_std.values, c='black')\n",
    "ax6.plot(mass_seq, pp_6, c='blue', lw=0.01)\n",
    "ax6.set_ylim(0, 1.1)\n",
    "ax6.set_title('$R^2: 1$')\n",
    "ax6.set_xlabel('Body mass (KG)')\n",
    "ax6.set_ylabel('Brain Volume (cc)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33e8c2e",
   "metadata": {},
   "source": [
    "### R Code 7.11 - Pag 201"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "afc62448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.324444\n",
       "1    0.334815\n",
       "2    0.453333\n",
       "3    0.385926\n",
       "4    0.557037\n",
       "5    0.645185\n",
       "6    1.000000\n",
       "Name: brain_std, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.brain_std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a5390d",
   "metadata": {},
   "source": [
    "Deletando a linha $i=3$ do dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4b5e6864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.324444\n",
       "1    0.334815\n",
       "2    0.453333\n",
       "4    0.557037\n",
       "5    0.645185\n",
       "6    1.000000\n",
       "Name: brain_std, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_deleted_line_3 = d.brain_std.drop(3)\n",
    "d_deleted_line_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f54627fe",
   "metadata": {},
   "source": [
    "### R Code 7.12 - Pag 206"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea96f1f",
   "metadata": {},
   "source": [
    "**Entropia da informação**:  *A incerteza contida na distribuição de probabilidade* é a média do $log$ da probabilidade do evento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e17d37af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def H(p):\n",
    "    \"\"\"Information Entropy\n",
    "    H(p):= -sum(p_i * log(pi))\n",
    "    \"\"\"\n",
    "    if not np.sum(p) == 1:\n",
    "        print('ProbabilityError: This is not probability, its sum not equal to 1.')\n",
    "    else:\n",
    "        return - np.sum([p_i * np.log(p_i) if p_i > 0 else 0 for p_i in p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7169c75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6108643020548935"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (0.3, 0.7)  # (p_rain, p_sum)\n",
    "\n",
    "H(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc66cfb3",
   "metadata": {},
   "source": [
    "A incerteza do clima de *Abu Dhabi* é menor, pois é pouco provável que chova."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1c2d86f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.056001534354847345"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_AbuDhabi = (0.01, 0.99)  # (p_rain, p_sum)\n",
    "\n",
    "H(p_AbuDhabi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50ae1e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.818808456222877"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_3dim = (0.7, 0.15, 0.15)  # (p_1, p_2, p_3)\n",
    "\n",
    "H(p_3dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e975e0",
   "metadata": {},
   "source": [
    "### R Code 7.13 - pag 210"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae949555",
   "metadata": {},
   "source": [
    "References:  \n",
    "\n",
    "- https://stackoverflow.com/questions/49147905/how-to-extract-posterior-samples-of-log-likelihood-from-pystan\n",
    "\n",
    "- https://mc-stan.org/docs/2_20/functions-reference/normal-distribution.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc96178",
   "metadata": {},
   "source": [
    "**Obs**:\n",
    "\n",
    "Here I learned to use the two blocks in stan: *transformed parameters* and *generated quantities*.\n",
    "\n",
    "To calculate the **Deviance** it is necessary to make these changes. \n",
    "\n",
    "That's why I did all 6 estimates again with the calculations needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ece392d7",
   "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 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.9e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 8e-06 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\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",
    "        mu = alpha + beta * body;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        beta ~ normal(0, 10);\n",
    "        sigma ~ lognormal(0, 1);    \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;\n",
    "        vector[N] brain_hat;\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(brain[i] | mu[i], sigma);\n",
    "            brain_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "        \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_1 = stan.build(model, data=data)\n",
    "samples_1 = posteriori_1.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb2d814e",
   "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_r8ixtt9p/model_szw6dlh7.stan', line 10, 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 1.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.4e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.5e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[2];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters{\n",
    "        vector[N] mu;\n",
    "        mu = alpha + beta[1] * body + \n",
    "                     beta[2] * body^2;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:2){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 10);    \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_2 = stan.build(model, data=data)\n",
    "samples_2 = posteriori_2.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "052d9762",
   "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_1ipahraa/model_a6y4smla.stan', line 10, 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 1.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 1.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[3];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3;\n",
    "    }\n",
    "    \n",
    "    model {                     \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:3){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 10);   \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_3 = stan.build(model, data=data)\n",
    "samples_3 = posteriori_3.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c31ba65b",
   "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_y03vqbot/model_5q3b5yyn.stan', line 10, 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   9% (700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  34% (2700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  59% (4700/8000)\n",
      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  84% (6700/8000)\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 1.6e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.16 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 1.4e-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 1.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[4];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3 +\n",
    "                     beta[4] * body^4;\n",
    "    }\n",
    "    \n",
    "    model {\n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:4){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 10); \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);   \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_4 = stan.build(model, data=data)\n",
    "samples_4 = posteriori_4.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f8bbacac",
   "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_23fy4d7r/model_4rwldby2.stan', line 10, 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",
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      "\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 1.8e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.\n",
      "  Adjust your expectations accordingly!\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",
      "  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 1.2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[5];\n",
    "        real<lower=0> sigma;\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3 +\n",
    "                     beta[4] * body^4 +\n",
    "                     beta[5] * body^5;\n",
    "    }\n",
    "    \n",
    "    model {          \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:5){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }\n",
    "        sigma ~ lognormal(0, 10);    \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, sigma);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_lik;\n",
    "        vector[N] brain_hat;\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            log_lik[i] = normal_lpdf(brain[i] | mu[i], sigma);\n",
    "            brain_hat[i] = normal_rng(mu[i], sigma);\n",
    "        }\n",
    "        \n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_5 = stan.build(model, data=data)\n",
    "samples_5 = posteriori_5.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "733b2e98",
   "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_17n24ee8/model_exxiy3jy.stan', line 10, 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_17n24ee8/model_exxiy3jy.stan', line 32, column 27: Argument\n",
      "    0.001 suggests there may be parameters that are not unit scale; consider\n",
      "    rescaling with a multiplier (see manual section 22.12).\n",
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      "\u001b[1A\u001b[0J\u001b[36mSampling:\u001b[0m  86% (6900/8000)\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 2.3e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.23 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",
      "  Gradient evaluation took 1.7e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Gradient evaluation took 2e-05 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.\n",
      "  Adjust your expectations accordingly!\n"
     ]
    }
   ],
   "source": [
    "model = \"\"\"\n",
    "    data {\n",
    "        int N;\n",
    "        vector[N] brain;\n",
    "        vector[N] body;\n",
    "    }\n",
    "    \n",
    "    parameters {\n",
    "        real alpha;\n",
    "        real beta[6];\n",
    "    }\n",
    "    \n",
    "    transformed parameters {\n",
    "        vector[N] mu;\n",
    "        \n",
    "        mu = alpha + beta[1] * body   + \n",
    "                     beta[2] * body^2 +\n",
    "                     beta[3] * body^3 +\n",
    "                     beta[4] * body^4 +\n",
    "                     beta[5] * body^5 +\n",
    "                     beta[6] * body^6;\n",
    "    }\n",
    "    \n",
    "    model {          \n",
    "        // Prioris\n",
    "        alpha ~ normal(0.5, 1);\n",
    "        for (j in 1:6){\n",
    "            beta[j] ~ normal(0, 10);\n",
    "        }    \n",
    "        \n",
    "        // Likelihood\n",
    "        brain ~ normal(mu, 0.001);\n",
    "    }\n",
    "    \n",
    "    generated quantities {\n",
    "        vector[N] log_ll_y;\n",
    "        \n",
    "        for (i in 1:N){\n",
    "            log_ll_y[i] = normal_lpdf(brain[i] | mu, 0.001);\n",
    "        }\n",
    "    }\n",
    "\"\"\"\n",
    "\n",
    "data = {\n",
    "    'N': len(d.brain_std),\n",
    "    'brain': d.brain_std.values,\n",
    "    'body': d.mass_std.values,\n",
    "}\n",
    "\n",
    "posteriori_6 = stan.build(model, data=data)\n",
    "samples_6 = posteriori_6.sample(num_chains=4, num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9d7ab29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lppd(samples, outcome, std_residuals=True):\n",
    "    \"\"\" Calculate the LOG-POINTWISE-PREDICTIVE-DENSITY\n",
    "    \n",
    "    samples : stan\n",
    "        Sampler results of fit posteriori. \n",
    "        Need 'mu' already computed.\n",
    "    \n",
    "    outcome : list\n",
    "        List with outcomes the original data.\n",
    "        \n",
    "    std_residuals : booleans\n",
    "        Compute lppd using std of the residuals \n",
    "    \"\"\"\n",
    "    mu = samples['mu']\n",
    "    N = len(outcome)\n",
    "    K = np.shape(mu)[1]  # Qty samples (mu) sampled from posteriori\n",
    "\n",
    "    outcome = outcome.reshape(-1, 1)\n",
    "\n",
    "    if std_residuals:\n",
    "        sigma = (np.sum((mu - outcome)**2, 0) / N)**0.5    \n",
    "    else:\n",
    "        sigma = samples['sigma'].flatten()\n",
    "        \n",
    "    lppd = np.empty(N, dtype=float)\n",
    "       \n",
    "    for i in range(N):\n",
    "        log_posteriori_predictive = stats.norm.logpdf(outcome[i], mu[i], sigma)\n",
    "\n",
    "        lppd[i] = np.log(np.sum(np.exp(log_posteriori_predictive))) - np.log(K)\n",
    "    \n",
    "    return lppd    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b6cf201",
   "metadata": {},
   "source": [
    "### R Code 7.15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad6dfa5",
   "metadata": {},
   "source": [
    "Like the book and the [this](https://github.com/pymc-devs/pymc-resources/blob/main/Rethinking_2/Chp_07.ipynb) one the values here have slight differeces. \n",
    "\n",
    "*Need review calculations - pag 211 - 2ed*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b68450d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.9566327201814353,\n",
       " 1.941588551424057,\n",
       " 2.14378226869432,\n",
       " 2.754228507821768,\n",
       " 5.480033073131333,\n",
       " 40.03033955975213]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lppd_lm = []\n",
    "\n",
    "lppd_lm.append(np.sum(lppd(samples_1, d.brain_std.values, std_residuals=True)))\n",
    "lppd_lm.append(np.sum(lppd(samples_2, d.brain_std.values, std_residuals=True)))\n",
    "lppd_lm.append(np.sum(lppd(samples_3, d.brain_std.values, std_residuals=True)))\n",
    "lppd_lm.append(np.sum(lppd(samples_4, d.brain_std.values, std_residuals=True)))\n",
    "lppd_lm.append(np.sum(lppd(samples_5, d.brain_std.values, std_residuals=True)))\n",
    "lppd_lm.append(np.sum(lppd(samples_6, d.brain_std.values, std_residuals=True)))\n",
    "\n",
    "lppd_lm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "75fe263d",
   "metadata": {},
   "outputs": [],
   "source": [
    "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b08ed219",
   "metadata": {},
   "outputs": [
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (chain: 4, draw: 1000, beta_dim_0: 5, mu_dim_0: 7)\n",
       "Coordinates:\n",
       "  * chain       (chain) int64 0 1 2 3\n",
       "  * draw        (draw) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "  * beta_dim_0  (beta_dim_0) int64 0 1 2 3 4\n",
       "  * mu_dim_0    (mu_dim_0) int64 0 1 2 3 4 5 6\n",
       "Data variables:\n",
       "    alpha       (chain, draw) float64 0.7899 0.4723 0.1157 ... 0.7226 1.419\n",
       "    beta        (chain, draw, beta_dim_0) float64 -1.853 -0.7106 ... 0.8986\n",
       "    sigma       (chain, draw) float64 0.7009 0.3478 0.3989 ... 0.1128 0.1313\n",
       "    mu          (chain, draw, mu_dim_0) float64 0.7289 0.2776 ... 0.533 1.005\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.503098\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</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-003b242a-7dd3-4b0c-97cd-d876847dbf84' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-003b242a-7dd3-4b0c-97cd-d876847dbf84' 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'>beta_dim_0</span>: 5</li><li><span class='xr-has-index'>mu_dim_0</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f3c2bc63-a444-4a96-b4a9-573638cdf997' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3c2bc63-a444-4a96-b4a9-573638cdf997' 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-6f574ebd-2659-4174-90b7-d0329b5c3117' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f574ebd-2659-4174-90b7-d0329b5c3117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75570d11-57f4-491e-b75e-ac4269fa2c62' class='xr-var-data-in' type='checkbox'><label for='data-75570d11-57f4-491e-b75e-ac4269fa2c62' 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-5767e646-6ea8-4234-af55-344751c90431' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5767e646-6ea8-4234-af55-344751c90431' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b15a94b8-1705-42d1-8a3c-e189e7203d66' class='xr-var-data-in' type='checkbox'><label for='data-b15a94b8-1705-42d1-8a3c-e189e7203d66' 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'>beta_dim_0</span></div><div class='xr-var-dims'>(beta_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-fc01248f-d59c-4b1d-9365-7b839d1a1fe9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc01248f-d59c-4b1d-9365-7b839d1a1fe9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda74593-d8a3-4002-aab8-3d06e89a8443' class='xr-var-data-in' type='checkbox'><label for='data-eda74593-d8a3-4002-aab8-3d06e89a8443' 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])</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</div><input id='attrs-458bd315-b083-4e28-8a74-c192172536be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-458bd315-b083-4e28-8a74-c192172536be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8f73d66-69f5-4db3-b9b9-c34340ce9ca1' class='xr-var-data-in' type='checkbox'><label for='data-a8f73d66-69f5-4db3-b9b9-c34340ce9ca1' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c2b0921-987f-45c6-b8d1-812e26a96fd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c2b0921-987f-45c6-b8d1-812e26a96fd0' 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'>0.7899 0.4723 ... 0.7226 1.419</div><input id='attrs-6f59dd99-3174-4912-b6f2-c1393dee08b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f59dd99-3174-4912-b6f2-c1393dee08b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06291d8c-3da1-4682-b4d3-60b7b4160eb4' class='xr-var-data-in' type='checkbox'><label for='data-06291d8c-3da1-4682-b4d3-60b7b4160eb4' 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([[ 0.78987293,  0.47233452,  0.11573143, ...,  0.7792361 ,\n",
       "         0.7792361 ,  0.7792361 ],\n",
       "       [ 1.01331428,  1.02187202,  0.8845154 , ...,  1.09216952,\n",
       "         0.72849058,  0.75621562],\n",
       "       [-0.77690797,  0.78264016,  0.0248647 , ...,  0.71938448,\n",
       "         0.68199666,  0.59996863],\n",
       "       [ 1.02186661,  1.14724822,  1.26341696, ...,  1.26102784,\n",
       "         0.72261203,  1.41932641]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw, beta_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.853 -0.7106 ... 0.9055 0.8986</div><input id='attrs-10613336-6191-4fbc-8cb7-2b6b1da4e3e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10613336-6191-4fbc-8cb7-2b6b1da4e3e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f37b01-008e-48db-96bc-0683a22f6129' class='xr-var-data-in' type='checkbox'><label for='data-73f37b01-008e-48db-96bc-0683a22f6129' 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([[[-1.85264604, -0.7105659 ,  2.77220961,  0.126569  ,\n",
       "         -0.67016469],\n",
       "        [-0.98779445, -0.01819979,  4.01543514,  0.77923001,\n",
       "         -2.23508551],\n",
       "        [-1.28918168, -0.72377396,  3.13577297,  1.24513507,\n",
       "         -1.77637242],\n",
       "        ...,\n",
       "        [ 1.98218068,  0.07544162, -3.49192098, -0.36988237,\n",
       "          1.45923271],\n",
       "        [ 1.98218068,  0.07544162, -3.49192098, -0.36988237,\n",
       "          1.45923271],\n",
       "        [ 1.98218068,  0.07544162, -3.49192098, -0.36988237,\n",
       "          1.45923271]],\n",
       "\n",
       "       [[ 1.8700364 , -0.58236774, -3.27650175, -0.0797799 ,\n",
       "          1.35473859],\n",
       "        [ 1.82111435, -0.5074553 , -3.21928236, -0.16178733,\n",
       "          1.37306453],\n",
       "        [ 2.0044353 , -0.33640789, -3.53766117, -0.19579082,\n",
       "          1.46457748],\n",
       "...\n",
       "        [-0.75352365,  0.05556392,  1.23676844, -0.39631079,\n",
       "         -0.25550218],\n",
       "        [-0.13320207,  0.20417305,  0.68549199, -0.14222021,\n",
       "         -0.27238981],\n",
       "        [ 0.06648903,  0.01576798,  0.63453191, -0.06929369,\n",
       "         -0.30563343]],\n",
       "\n",
       "       [[ 1.62438068,  0.35683278, -3.27142503, -1.04963209,\n",
       "          1.80991864],\n",
       "        [ 1.34328151,  0.0511817 , -3.36710357, -1.12610104,\n",
       "          1.9829131 ],\n",
       "        [ 1.46399565, -0.28358385, -2.92408998, -0.70225059,\n",
       "          1.53345295],\n",
       "        ...,\n",
       "        [ 1.45964569, -1.62303603, -2.10083532,  0.78522526,\n",
       "          0.60857052],\n",
       "        [ 0.98134573,  0.11795768, -1.61655869, -0.2982359 ,\n",
       "          0.72860781],\n",
       "        [ 1.87211942, -2.03014332, -2.9094564 ,  0.90545583,\n",
       "          0.89859179]]])</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'>0.7009 0.3478 ... 0.1128 0.1313</div><input id='attrs-36a4d82d-6812-4ae9-b008-d0fbdfd3a46c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36a4d82d-6812-4ae9-b008-d0fbdfd3a46c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-495c463d-6f69-43ef-a913-b90e75b75005' class='xr-var-data-in' type='checkbox'><label for='data-495c463d-6f69-43ef-a913-b90e75b75005' 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([[0.70090975, 0.34781307, 0.3988521 , ..., 0.09006732, 0.09006732,\n",
       "        0.09006732],\n",
       "       [0.04488868, 0.04896691, 0.04814223, ..., 0.04552586, 0.04958059,\n",
       "        0.04446534],\n",
       "       [0.56074654, 1.42463415, 0.67589924, ..., 0.28199782, 0.25907059,\n",
       "        0.23322822],\n",
       "       [0.21959001, 0.23356949, 0.27390146, ..., 0.12010018, 0.11277224,\n",
       "        0.13131567]])</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'>0.7289 0.2776 ... 0.533 1.005</div><input id='attrs-446c4cfd-8ed8-4398-a31f-940bc69bfaec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-446c4cfd-8ed8-4398-a31f-940bc69bfaec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c66ca1d5-aa1d-4c6d-8082-83e2d408220d' class='xr-var-data-in' type='checkbox'><label for='data-c66ca1d5-aa1d-4c6d-8082-83e2d408220d' 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([[[ 7.28919717e-01,  2.77567058e-01, -7.87697729e-02, ...,\n",
       "          2.86121279e-01,  1.31034710e+00,  3.71185270e-02],\n",
       "        [ 2.60352419e-01,  2.66808219e-01,  4.69848718e-01, ...,\n",
       "          1.74932412e+00,  7.75841089e-01,  1.07404277e+00],\n",
       "        [ 1.66573415e-01,  3.03598139e-01,  6.05509856e-01, ...,\n",
       "          4.71592155e-01,  6.02265414e-01,  1.71197116e-03],\n",
       "        ...,\n",
       "        [ 3.77323899e-01,  5.09914842e-01,  5.33673820e-01, ...,\n",
       "          6.52310593e-01,  6.77732648e-01,  1.09823446e+00],\n",
       "        [ 3.77323899e-01,  5.09914842e-01,  5.33673820e-01, ...,\n",
       "          6.52310593e-01,  6.77732648e-01,  1.09823446e+00],\n",
       "        [ 3.77323899e-01,  5.09914842e-01,  5.33673820e-01, ...,\n",
       "          6.52310593e-01,  6.77732648e-01,  1.09823446e+00]],\n",
       "\n",
       "       [[ 3.34287096e-01,  4.00452975e-01,  3.99923091e-01, ...,\n",
       "          5.33886880e-01,  6.19905523e-01,  1.04289140e+00],\n",
       "        [ 3.63849334e-01,  4.02868641e-01,  3.71279614e-01, ...,\n",
       "          5.58594290e-01,  6.46178378e-01,  1.05860877e+00],\n",
       "        [ 2.99417529e-01,  4.03367719e-01,  4.19049991e-01, ...,\n",
       "          5.22966599e-01,  5.92824205e-01,  1.03168802e+00],\n",
       "...\n",
       "        [ 6.82004262e-01,  3.88811698e-01,  1.23127713e-01, ...,\n",
       "          5.82901220e-01,  2.12202291e-01,  5.15716322e-01],\n",
       "        [ 6.11113652e-01,  5.23292485e-01,  4.57755981e-01, ...,\n",
       "          9.82945842e-01,  7.12840550e-01,  8.65737066e-01],\n",
       "        [ 3.19583035e-01,  2.12134858e-01,  1.45117502e-01, ...,\n",
       "          9.16319042e-01,  4.92467674e-01,  8.22746303e-01]],\n",
       "\n",
       "       [[ 6.13563851e-01,  4.39150199e-01,  1.27206687e-01, ...,\n",
       "          7.20217332e-01,  8.73502172e-01,  1.19450541e+00],\n",
       "        [ 7.39656662e-01,  4.72799666e-01,  6.34941484e-02, ...,\n",
       "          3.15121018e-01,  3.98596403e-01,  9.26003605e-01],\n",
       "        [ 6.34321562e-01,  4.46322393e-01,  1.70795934e-01, ...,\n",
       "          6.10366151e-01,  4.04610087e-01,  1.15273707e+00],\n",
       "        ...,\n",
       "        [ 2.46841834e-01,  3.38333776e-01,  4.70868706e-01, ...,\n",
       "          5.64570460e-01,  7.59597168e-01,  9.85634704e-01],\n",
       "        [ 4.75187567e-01,  4.85110184e-01,  4.42240000e-01, ...,\n",
       "          7.36703062e-01,  7.23423521e-01,  9.36206274e-01],\n",
       "        [ 1.80165051e-01,  2.96553153e-01,  4.50409276e-01, ...,\n",
       "          4.08298078e-01,  5.32963684e-01,  1.00461600e+00]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9965bb6-11f4-41f0-8c86-9f6655344110' class='xr-section-summary-in' type='checkbox'  ><label for='section-c9965bb6-11f4-41f0-8c86-9f6655344110' 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-9c5fd117-6d8d-4ab3-bbd4-1e8da5f3e996' class='xr-index-data-in' type='checkbox'/><label for='index-9c5fd117-6d8d-4ab3-bbd4-1e8da5f3e996' 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-37146579-5188-4841-a064-5d4b9a812bed' class='xr-index-data-in' type='checkbox'/><label for='index-37146579-5188-4841-a064-5d4b9a812bed' 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>beta_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5516c2ab-2b4b-4084-b7c7-5811f1f8246a' class='xr-index-data-in' type='checkbox'/><label for='index-5516c2ab-2b4b-4084-b7c7-5811f1f8246a' 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], dtype=&#x27;int64&#x27;, name=&#x27;beta_dim_0&#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-3020cf3f-4fb4-4ab4-b1a7-23654d81fd16' class='xr-index-data-in' type='checkbox'/><label for='index-3020cf3f-4fb4-4ab4-b1a7-23654d81fd16' 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], dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75023d56-16f2-4bd6-8a75-b7cbb26e0046' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75023d56-16f2-4bd6-8a75-b7cbb26e0046' class='xr-section-summary' >Attributes: <span>(9)</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>2023-08-11T18:53:39.503098</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></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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       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
       ".xr-header > div,\n",
       ".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 > span {\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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       "  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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       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\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",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\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",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\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",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       ".xr-var-data,\n",
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       "\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\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",
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       "\n",
       "dl.xr-attrs {\n",
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       "  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",
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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, brain_hat_dim_0: 7)\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",
       "  * brain_hat_dim_0  (brain_hat_dim_0) int64 0 1 2 3 4 5 6\n",
       "Data variables:\n",
       "    brain_hat        (chain, draw, brain_hat_dim_0) float64 -0.6716 ... 1.096\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.659568\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-5a974bf8-e4c8-4ff3-b6d2-701c39d94690' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a974bf8-e4c8-4ff3-b6d2-701c39d94690' 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'>brain_hat_dim_0</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f419c35a-a42c-4c51-b0ce-b14dff3395ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f419c35a-a42c-4c51-b0ce-b14dff3395ae' 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-0f94c570-486e-4d29-86dd-a97903f62d82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f94c570-486e-4d29-86dd-a97903f62d82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d543efc-1612-474c-a097-f7beded0e002' class='xr-var-data-in' type='checkbox'><label for='data-7d543efc-1612-474c-a097-f7beded0e002' 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-03e636b0-ee72-47a6-bbf9-f9249fbec54d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03e636b0-ee72-47a6-bbf9-f9249fbec54d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a4bd57b-4fa7-417c-b765-f7d8a6b8509c' class='xr-var-data-in' type='checkbox'><label for='data-8a4bd57b-4fa7-417c-b765-f7d8a6b8509c' 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'>brain_hat_dim_0</span></div><div class='xr-var-dims'>(brain_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</div><input id='attrs-8c1e70e4-96ad-4dac-b43d-0bc8c3b373a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c1e70e4-96ad-4dac-b43d-0bc8c3b373a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-507dfacb-9593-4aa2-bd75-0f4fcd289f3c' class='xr-var-data-in' type='checkbox'><label for='data-507dfacb-9593-4aa2-bd75-0f4fcd289f3c' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef6fa451-a013-432e-9a41-bd118651a653' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef6fa451-a013-432e-9a41-bd118651a653' 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>brain_hat</span></div><div class='xr-var-dims'>(chain, draw, brain_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6716 0.7404 ... 0.5696 1.096</div><input id='attrs-22765990-dff7-43c7-bb3b-437a604eeeb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22765990-dff7-43c7-bb3b-437a604eeeb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-904a93b8-a1e4-49f5-a3ef-a1217edba719' class='xr-var-data-in' type='checkbox'><label for='data-904a93b8-a1e4-49f5-a3ef-a1217edba719' 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([[[-0.67157625,  0.74044835, -0.05370771, ...,  0.41218331,\n",
       "          0.31817051, -0.56809302],\n",
       "        [ 0.14376081,  0.76836261,  0.67444754, ...,  1.63616778,\n",
       "          0.33656255,  1.07186457],\n",
       "        [ 0.0949888 ,  0.13368191,  0.27441397, ...,  1.14491828,\n",
       "          0.34147127, -0.17442567],\n",
       "        ...,\n",
       "        [ 0.33247667,  0.46912963,  0.50955097, ...,  0.65775759,\n",
       "          0.69695317,  0.8870647 ],\n",
       "        [ 0.38427196,  0.40753803,  0.57867721, ...,  0.64500619,\n",
       "          0.69391179,  1.12743   ],\n",
       "        [ 0.27838681,  0.41336347,  0.69728829, ...,  0.64036479,\n",
       "          0.66338501,  1.01368887]],\n",
       "\n",
       "       [[ 0.38461929,  0.43157113,  0.39087932, ...,  0.52886506,\n",
       "          0.61978389,  1.08284428],\n",
       "        [ 0.27913648,  0.4830043 ,  0.39271807, ...,  0.49234028,\n",
       "          0.71803543,  1.00321182],\n",
       "        [ 0.30125609,  0.39467348,  0.4280089 , ...,  0.33363188,\n",
       "          0.67734479,  1.05794528],\n",
       "...\n",
       "        [ 0.90842314,  0.48742629,  0.50223053, ...,  1.11775084,\n",
       "          0.45654165,  0.62486554],\n",
       "        [ 0.6143996 ,  0.24072102,  0.4114903 , ...,  1.51608438,\n",
       "          0.54100887,  0.80509306],\n",
       "        [ 0.46456858,  0.0884946 ,  0.57334588, ...,  0.41437296,\n",
       "          0.90743546,  0.78007579]],\n",
       "\n",
       "       [[ 0.63935769,  0.06836551, -0.015311  , ...,  1.15595971,\n",
       "          1.05760951,  1.2312529 ],\n",
       "        [ 0.35213352,  0.197653  ,  0.29754935, ...,  0.47394989,\n",
       "          0.62338563,  0.65766756],\n",
       "        [ 1.1548429 ,  0.25337811,  0.02290543, ...,  0.4648817 ,\n",
       "          0.05834947,  1.08793272],\n",
       "        ...,\n",
       "        [ 0.34564091,  0.37021532,  0.44659541, ...,  0.42250087,\n",
       "          0.67991483,  1.07335426],\n",
       "        [ 0.27292318,  0.6111944 ,  0.6369111 , ...,  0.76217779,\n",
       "          1.02446786,  0.83080833],\n",
       "        [ 0.28299893,  0.3752658 ,  0.63134444, ...,  0.64706566,\n",
       "          0.56957333,  1.0960166 ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c309098-fa23-4072-a56a-6d57da71d21e' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c309098-fa23-4072-a56a-6d57da71d21e' 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-fcfc5e30-3a2c-4f23-bc58-0c2be4043070' class='xr-index-data-in' type='checkbox'/><label for='index-fcfc5e30-3a2c-4f23-bc58-0c2be4043070' 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-35953130-7e18-4992-bfb4-a8ccbbda91ca' class='xr-index-data-in' type='checkbox'/><label for='index-35953130-7e18-4992-bfb4-a8ccbbda91ca' 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>brain_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fee43fb3-6331-463a-b7a4-af99f17fd86d' class='xr-index-data-in' type='checkbox'/><label for='index-fee43fb3-6331-463a-b7a4-af99f17fd86d' 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], dtype=&#x27;int64&#x27;, name=&#x27;brain_hat_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-564230f9-b5bb-4ba3-9654-ed8d09fc988d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-564230f9-b5bb-4ba3-9654-ed8d09fc988d' 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>2023-08-11T18:53:39.659568</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_likelihood3a68fd73-0af0-4d75-8ac2-084c9ec20929\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihood3a68fd73-0af0-4d75-8ac2-084c9ec20929\" 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",
       "<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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       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\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",
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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",
       "}\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",
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       "\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, brain_dim_0: 7)\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",
       "  * brain_dim_0  (brain_dim_0) int64 0 1 2 3 4 5 6\n",
       "Data variables:\n",
       "    brain        (chain, draw, brain_dim_0) float64 -0.7301 -0.5669 ... 1.111\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.614761\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-bd3c6fb6-243c-447c-8a2a-9058bba172d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd3c6fb6-243c-447c-8a2a-9058bba172d1' 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'>brain_dim_0</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39d9a7f1-41bb-409a-8cad-7f7441f1cf91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39d9a7f1-41bb-409a-8cad-7f7441f1cf91' 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-c9724bcd-d407-468d-99e7-c856f2e1995d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9724bcd-d407-468d-99e7-c856f2e1995d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6216396f-04a7-436e-9e6b-afe2b91f47eb' class='xr-var-data-in' type='checkbox'><label for='data-6216396f-04a7-436e-9e6b-afe2b91f47eb' 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-a282eefe-e4bc-4569-ada0-3529e10b5c5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a282eefe-e4bc-4569-ada0-3529e10b5c5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee0d61c4-70b7-4962-9418-02cc768df248' class='xr-var-data-in' type='checkbox'><label for='data-ee0d61c4-70b7-4962-9418-02cc768df248' 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'>brain_dim_0</span></div><div class='xr-var-dims'>(brain_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6</div><input id='attrs-abf5cdf6-5f27-48ac-bb3b-a9c9176b9854' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abf5cdf6-5f27-48ac-bb3b-a9c9176b9854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7321250a-33ae-4b83-8f42-a0b4c1b06a01' class='xr-var-data-in' type='checkbox'><label for='data-7321250a-33ae-4b83-8f42-a0b4c1b06a01' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1296cc7c-974d-4b44-98a4-74a0cd923382' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1296cc7c-974d-4b44-98a4-74a0cd923382' 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>brain</span></div><div class='xr-var-dims'>(chain, draw, brain_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.7301 -0.5669 ... 0.746 1.111</div><input id='attrs-1c3d954e-4567-40bd-a7cf-6417c96a1f84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c3d954e-4567-40bd-a7cf-6417c96a1f84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55f641ad-5ead-4dd0-8c64-4f08410cc6ab' class='xr-var-data-in' type='checkbox'><label for='data-55f641ad-5ead-4dd0-8c64-4f08410cc6ab' 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([[[-7.30068341e-01, -5.66897907e-01, -8.51724842e-01, ...,\n",
       "         -6.38261315e-01, -1.01386095e+00, -1.50717005e+00],\n",
       "        [ 1.20173589e-01,  1.18036313e-01,  1.36024235e-01, ...,\n",
       "         -5.73828167e+00,  6.65951965e-02,  1.14492434e-01],\n",
       "        [-7.81080838e-02, -2.83674445e-03, -7.25588796e-02, ...,\n",
       "         -2.27205375e-02, -5.56370418e-03, -3.13203502e+00],\n",
       "        ...,\n",
       "        [ 1.31591011e+00, -4.01506396e-01,  1.09042304e+00, ...,\n",
       "          9.28784878e-01,  1.42296591e+00,  8.93469865e-01],\n",
       "        [ 1.31591011e+00, -4.01506396e-01,  1.09042304e+00, ...,\n",
       "          9.28784878e-01,  1.42296591e+00,  8.93469865e-01],\n",
       "        [ 1.31591011e+00, -4.01506396e-01,  1.09042304e+00, ...,\n",
       "          9.28784878e-01,  1.42296591e+00,  8.93469865e-01]],\n",
       "\n",
       "       [[ 2.16059186e+00,  1.11555375e+00,  1.47677427e+00, ...,\n",
       "          2.05164561e+00,  2.02605458e+00,  1.72813531e+00],\n",
       "        [ 1.77388096e+00,  1.13191083e+00,  6.93691124e-01, ...,\n",
       "          2.09716635e+00,  2.09746634e+00,  1.38138042e+00],\n",
       "        [ 1.97953314e+00,  1.10081715e+00,  1.86109520e+00, ...,\n",
       "          1.86423472e+00,  1.52318641e+00,  1.89803239e+00],\n",
       "...\n",
       "        [-4.56934270e-01,  3.28585167e-01, -3.38645684e-01, ...,\n",
       "          3.42711356e-01, -8.31827936e-01, -1.12769600e+00],\n",
       "        [-1.80487408e-01,  1.67077330e-01,  4.31570453e-01, ...,\n",
       "         -9.19631222e-01,  3.97617432e-01,  2.97425436e-01],\n",
       "        [ 5.36582065e-01,  3.98456983e-01, -3.36408697e-01, ...,\n",
       "         -6.49730780e-01,  3.22418695e-01,  2.47998479e-01]],\n",
       "\n",
       "       [[-2.69706428e-01,  4.84176855e-01, -5.05797947e-01, ...,\n",
       "          3.20945718e-01,  5.65226674e-02,  2.04763636e-01],\n",
       "        [-1.04473911e+00,  3.60835046e-01, -8.57527069e-01, ...,\n",
       "         -1.03604572e-03, -2.19569449e-02,  4.85153863e-01],\n",
       "        [-2.63922705e-01,  2.93179724e-01, -1.55978078e-01, ...,\n",
       "          3.57093938e-01, -9.68098218e-03,  2.20569887e-01],\n",
       "        ...,\n",
       "        [ 9.91736278e-01,  1.20006130e+00,  1.18983165e+00, ...,\n",
       "          1.19852327e+00,  7.46731041e-01,  1.19333716e+00],\n",
       "        [ 3.70057543e-01,  3.75356947e-01,  1.25860825e+00, ...,\n",
       "         -5.65829205e-03,  1.02278603e+00,  1.10344595e+00],\n",
       "        [ 5.07617798e-01,  1.06876389e+00,  1.11096469e+00, ...,\n",
       "          4.69727833e-01,  7.46047704e-01,  1.11059478e+00]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0206692e-7314-428e-923a-a042ad7cba82' class='xr-section-summary-in' type='checkbox'  ><label for='section-0206692e-7314-428e-923a-a042ad7cba82' 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-50c56df4-cc0e-4551-a1a0-e4f3af291561' class='xr-index-data-in' type='checkbox'/><label for='index-50c56df4-cc0e-4551-a1a0-e4f3af291561' 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-74106639-d6ff-4ffa-ab23-83aad98bc93b' class='xr-index-data-in' type='checkbox'/><label for='index-74106639-d6ff-4ffa-ab23-83aad98bc93b' 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>brain_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16cea141-9d6a-441c-8966-2333b7d526d9' class='xr-index-data-in' type='checkbox'/><label for='index-16cea141-9d6a-441c-8966-2333b7d526d9' 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], dtype=&#x27;int64&#x27;, name=&#x27;brain_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dec92ad0-4c65-4209-bfaf-ba30892bb03d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dec92ad0-4c65-4209-bfaf-ba30892bb03d' 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>2023-08-11T18:53:39.614761</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_stats8816d03d-c490-4e0e-abf0-900cdf680e28\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats8816d03d-c490-4e0e-abf0-900cdf680e28\" 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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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
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       "\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",
       "    acceptance_rate  (chain, draw) float64 0.9942 0.9932 1.0 ... 0.7437 0.6009\n",
       "    step_size        (chain, draw) float64 0.02283 0.02283 ... 0.01823 0.01823\n",
       "    tree_depth       (chain, draw) int64 6 7 5 4 8 6 5 4 5 ... 5 6 7 7 5 7 8 7 7\n",
       "    n_steps          (chain, draw) int64 63 159 31 31 255 ... 47 127 255 255 191\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 2.459 5.23 0.8842 ... -7.189 -5.73\n",
       "    lp               (chain, draw) float64 -1.21 0.03107 2.038 ... 10.68 9.605\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.561999\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</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-e2571be2-7ba3-4418-bb9e-de6d545835a7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2571be2-7ba3-4418-bb9e-de6d545835a7' 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-67529e59-9aaf-41fa-a065-677478cb5846' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67529e59-9aaf-41fa-a065-677478cb5846' 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-8d1c9b80-111f-40ae-a574-35c19a951261' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d1c9b80-111f-40ae-a574-35c19a951261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a372982-0738-4d61-ab96-a7ea8f762994' class='xr-var-data-in' type='checkbox'><label for='data-3a372982-0738-4d61-ab96-a7ea8f762994' 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-aaafe58f-babc-4d90-8ad5-086ec3f3fd6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaafe58f-babc-4d90-8ad5-086ec3f3fd6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e8f696e-5999-48ca-8401-13eac245a39a' class='xr-var-data-in' type='checkbox'><label for='data-6e8f696e-5999-48ca-8401-13eac245a39a' 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-2d9656a2-9a1c-4f62-8d1e-c8ade223f2b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d9656a2-9a1c-4f62-8d1e-c8ade223f2b0' 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'>0.9942 0.9932 1.0 ... 0.7437 0.6009</div><input id='attrs-cd4459e4-d8d2-44ad-a279-1e7bf1b1c579' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd4459e4-d8d2-44ad-a279-1e7bf1b1c579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e03b770e-7cc0-4e81-aebf-ed857ee69672' class='xr-var-data-in' type='checkbox'><label for='data-e03b770e-7cc0-4e81-aebf-ed857ee69672' 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([[9.94233707e-01, 9.93165704e-01, 1.00000000e+00, ...,\n",
       "        2.58318986e-01, 1.48519049e-04, 6.71099767e-02],\n",
       "       [7.70774184e-01, 1.80592293e-02, 2.50854737e-01, ...,\n",
       "        2.98825241e-01, 8.26983265e-01, 4.41928941e-01],\n",
       "       [9.85851276e-01, 9.91385247e-01, 9.79344898e-01, ...,\n",
       "        8.60362083e-01, 6.29604725e-01, 6.21876881e-01],\n",
       "       [9.95387414e-01, 9.99909983e-01, 9.99330539e-01, ...,\n",
       "        9.87221552e-01, 7.43663516e-01, 6.00850499e-01]])</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'>0.02283 0.02283 ... 0.01823 0.01823</div><input id='attrs-211d9a31-2d3c-4bf0-85dd-7d721a997fac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-211d9a31-2d3c-4bf0-85dd-7d721a997fac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bda6ac5-b717-4488-a09d-88d2e273e43d' class='xr-var-data-in' type='checkbox'><label for='data-3bda6ac5-b717-4488-a09d-88d2e273e43d' 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([[0.02283011, 0.02283011, 0.02283011, ..., 0.02283011, 0.02283011,\n",
       "        0.02283011],\n",
       "       [0.00923471, 0.00923471, 0.00923471, ..., 0.00923471, 0.00923471,\n",
       "        0.00923471],\n",
       "       [0.02833399, 0.02833399, 0.02833399, ..., 0.02833399, 0.02833399,\n",
       "        0.02833399],\n",
       "       [0.01823457, 0.01823457, 0.01823457, ..., 0.01823457, 0.01823457,\n",
       "        0.01823457]])</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'>6 7 5 4 8 6 5 4 ... 6 7 7 5 7 8 7 7</div><input id='attrs-15aa66d3-b827-4bfc-928e-5bcc45de769c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15aa66d3-b827-4bfc-928e-5bcc45de769c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3505cad8-f196-4bbf-adc9-caf18ccef34a' class='xr-var-data-in' type='checkbox'><label for='data-3505cad8-f196-4bbf-adc9-caf18ccef34a' 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([[6, 7, 5, ..., 1, 1, 1],\n",
       "       [3, 6, 6, ..., 4, 7, 4],\n",
       "       [8, 8, 7, ..., 6, 6, 5],\n",
       "       [6, 5, 6, ..., 8, 7, 7]])</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'>63 159 31 31 ... 127 255 255 191</div><input id='attrs-8177ba7d-8e28-429b-8d2f-d0cd2fb1a52d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8177ba7d-8e28-429b-8d2f-d0cd2fb1a52d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb69ea1d-3d25-4c24-bd86-c32810aa2ff9' class='xr-var-data-in' type='checkbox'><label for='data-bb69ea1d-3d25-4c24-bd86-c32810aa2ff9' 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([[ 63, 159,  31, ...,   3,   2,   3],\n",
       "       [ 15,  63,  63, ...,  15, 127,  17],\n",
       "       [255, 511, 223, ...,  63,  63,  63],\n",
       "       [ 63,  31, 127, ..., 255, 255, 191]])</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-4d234ec5-f252-46c1-ab9a-d1e470a842e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d234ec5-f252-46c1-ab9a-d1e470a842e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c7fda9-f8c8-46c9-969b-e7a268129b37' class='xr-var-data-in' type='checkbox'><label for='data-d5c7fda9-f8c8-46c9-969b-e7a268129b37' 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, ...,  True,  True,  True],\n",
       "       [False, False, False, ..., False, False,  True],\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'>2.459 5.23 0.8842 ... -7.189 -5.73</div><input id='attrs-74bba4e9-605d-4113-bf9e-1075a64f5716' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74bba4e9-605d-4113-bf9e-1075a64f5716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf04c65-4579-4e45-a8b8-f464deb8d6ff' class='xr-var-data-in' type='checkbox'><label for='data-5cf04c65-4579-4e45-a8b8-f464deb8d6ff' 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([[  2.45889561,   5.23014587,   0.8842482 , ...,  -5.24178702,\n",
       "         -7.52119165,  -8.22620056],\n",
       "       [-12.63637198, -12.02113756, -11.70029831, ..., -13.51513333,\n",
       "        -13.30583019,  -3.70330084],\n",
       "       [  3.81284024,  12.21204895,  12.16631997, ...,   0.12223333,\n",
       "          1.31505589,  -4.03915439],\n",
       "       [ -2.14973909,  -2.49191708,  -0.2733382 , ...,  -6.040826  ,\n",
       "         -7.18920473,  -5.72972827]])</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'>-1.21 0.03107 2.038 ... 10.68 9.605</div><input id='attrs-cc0a8da2-a3bb-4058-99e2-10db6ba391cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc0a8da2-a3bb-4058-99e2-10db6ba391cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de8a2f23-5aa9-4369-8404-7e3a9213d461' class='xr-var-data-in' type='checkbox'><label for='data-de8a2f23-5aa9-4369-8404-7e3a9213d461' 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([[-1.2099401 ,  0.0310734 ,  2.03763653, ..., 10.35927788,\n",
       "        10.35927788, 10.35927788],\n",
       "       [17.94392058, 16.07488155, 14.50387307, ..., 16.51494071,\n",
       "        14.95817912, 13.73149783],\n",
       "       [-0.84080971, -7.14573667, -4.55199362, ...,  1.82542767,\n",
       "         5.26002971,  6.31266796],\n",
       "       [ 5.66941622,  3.1314764 ,  4.76632921, ..., 10.95431518,\n",
       "        10.68023411,  9.60531483]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-449f32e1-d103-4b52-adcc-69a6c5a8aa94' class='xr-section-summary-in' type='checkbox'  ><label for='section-449f32e1-d103-4b52-adcc-69a6c5a8aa94' 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-8d9c59d0-202b-4ec4-bf90-6ae5ed5a6f0f' class='xr-index-data-in' type='checkbox'/><label for='index-8d9c59d0-202b-4ec4-bf90-6ae5ed5a6f0f' 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-59e2caf2-93e6-43d4-ac3b-7758edba8d6c' class='xr-index-data-in' type='checkbox'/><label for='index-59e2caf2-93e6-43d4-ac3b-7758edba8d6c' 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-42833e27-cb6b-420c-b4b7-fc0ad4ab4618' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42833e27-cb6b-420c-b4b7-fc0ad4ab4618' class='xr-section-summary' >Attributes: <span>(9)</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>2023-08-11T18:53:39.561999</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></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\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"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = az.from_pystan(\n",
    "    posterior=samples_5,\n",
    "    posterior_predictive=\"brain_hat\",\n",
    "    observed_data=data,\n",
    "    log_likelihood={\"brain\": \"log_lik\"},\n",
    ")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fc6fd1e4",
   "metadata": {},
   "outputs": [
    {
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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",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".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:     (chain: 4, draw: 1000, beta_dim_0: 5, mu_dim_0: 7)\n",
       "Coordinates:\n",
       "  * chain       (chain) int64 0 1 2 3\n",
       "  * draw        (draw) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "  * beta_dim_0  (beta_dim_0) int64 0 1 2 3 4\n",
       "  * mu_dim_0    (mu_dim_0) int64 0 1 2 3 4 5 6\n",
       "Data variables:\n",
       "    alpha       (chain, draw) float64 0.7899 0.4723 0.1157 ... 0.7226 1.419\n",
       "    beta        (chain, draw, beta_dim_0) float64 -1.853 -0.7106 ... 0.8986\n",
       "    sigma       (chain, draw) float64 0.7009 0.3478 0.3989 ... 0.1128 0.1313\n",
       "    mu          (chain, draw, mu_dim_0) float64 0.7289 0.2776 ... 0.533 1.005\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.756266\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</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-96f8d786-c1fe-48d4-96d8-8fd0f79c6413' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96f8d786-c1fe-48d4-96d8-8fd0f79c6413' 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'>beta_dim_0</span>: 5</li><li><span class='xr-has-index'>mu_dim_0</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15ecb60e-f12c-4e02-8cf0-cc3a5629b548' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15ecb60e-f12c-4e02-8cf0-cc3a5629b548' 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-6f22947c-9bc6-4ca2-a959-2c6b1ae917fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f22947c-9bc6-4ca2-a959-2c6b1ae917fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7c3265-ff1e-44e9-ac78-1bf41ea1b305' class='xr-var-data-in' type='checkbox'><label for='data-ff7c3265-ff1e-44e9-ac78-1bf41ea1b305' 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-9a7513ea-a785-4004-a701-ba241499ba2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a7513ea-a785-4004-a701-ba241499ba2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c434cd15-ab93-4c14-b226-856471df1bdc' class='xr-var-data-in' type='checkbox'><label for='data-c434cd15-ab93-4c14-b226-856471df1bdc' 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'>beta_dim_0</span></div><div class='xr-var-dims'>(beta_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-adcd93d5-9861-4ea6-8cad-8729fb5ea6f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adcd93d5-9861-4ea6-8cad-8729fb5ea6f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f74c51a-09bc-4dd0-87b2-a331ed862ad2' class='xr-var-data-in' type='checkbox'><label for='data-4f74c51a-09bc-4dd0-87b2-a331ed862ad2' 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])</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</div><input id='attrs-c1790415-78b4-49e7-93e0-2a4a74ff133b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1790415-78b4-49e7-93e0-2a4a74ff133b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-084e1852-348b-43ef-9c3d-ca015bf027f9' class='xr-var-data-in' type='checkbox'><label for='data-084e1852-348b-43ef-9c3d-ca015bf027f9' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-675aa751-c254-4196-a90d-2142d3113e59' class='xr-section-summary-in' type='checkbox'  checked><label for='section-675aa751-c254-4196-a90d-2142d3113e59' 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'>0.7899 0.4723 ... 0.7226 1.419</div><input id='attrs-5b653d03-3b16-437b-b4c3-f928334d3515' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b653d03-3b16-437b-b4c3-f928334d3515' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cdb3878-4518-4d05-8311-7bae3d2efc6f' class='xr-var-data-in' type='checkbox'><label for='data-7cdb3878-4518-4d05-8311-7bae3d2efc6f' 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([[ 0.78987293,  0.47233452,  0.11573143, ...,  0.7792361 ,\n",
       "         0.7792361 ,  0.7792361 ],\n",
       "       [ 1.01331428,  1.02187202,  0.8845154 , ...,  1.09216952,\n",
       "         0.72849058,  0.75621562],\n",
       "       [-0.77690797,  0.78264016,  0.0248647 , ...,  0.71938448,\n",
       "         0.68199666,  0.59996863],\n",
       "       [ 1.02186661,  1.14724822,  1.26341696, ...,  1.26102784,\n",
       "         0.72261203,  1.41932641]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beta</span></div><div class='xr-var-dims'>(chain, draw, beta_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.853 -0.7106 ... 0.9055 0.8986</div><input id='attrs-0c4d2f23-bd21-429f-9fbd-5d3299e67c81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c4d2f23-bd21-429f-9fbd-5d3299e67c81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a62e6a3d-c823-4f37-b8ae-5a5c475df105' class='xr-var-data-in' type='checkbox'><label for='data-a62e6a3d-c823-4f37-b8ae-5a5c475df105' 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([[[-1.85264604, -0.7105659 ,  2.77220961,  0.126569  ,\n",
       "         -0.67016469],\n",
       "        [-0.98779445, -0.01819979,  4.01543514,  0.77923001,\n",
       "         -2.23508551],\n",
       "        [-1.28918168, -0.72377396,  3.13577297,  1.24513507,\n",
       "         -1.77637242],\n",
       "        ...,\n",
       "        [ 1.98218068,  0.07544162, -3.49192098, -0.36988237,\n",
       "          1.45923271],\n",
       "        [ 1.98218068,  0.07544162, -3.49192098, -0.36988237,\n",
       "          1.45923271],\n",
       "        [ 1.98218068,  0.07544162, -3.49192098, -0.36988237,\n",
       "          1.45923271]],\n",
       "\n",
       "       [[ 1.8700364 , -0.58236774, -3.27650175, -0.0797799 ,\n",
       "          1.35473859],\n",
       "        [ 1.82111435, -0.5074553 , -3.21928236, -0.16178733,\n",
       "          1.37306453],\n",
       "        [ 2.0044353 , -0.33640789, -3.53766117, -0.19579082,\n",
       "          1.46457748],\n",
       "...\n",
       "        [-0.75352365,  0.05556392,  1.23676844, -0.39631079,\n",
       "         -0.25550218],\n",
       "        [-0.13320207,  0.20417305,  0.68549199, -0.14222021,\n",
       "         -0.27238981],\n",
       "        [ 0.06648903,  0.01576798,  0.63453191, -0.06929369,\n",
       "         -0.30563343]],\n",
       "\n",
       "       [[ 1.62438068,  0.35683278, -3.27142503, -1.04963209,\n",
       "          1.80991864],\n",
       "        [ 1.34328151,  0.0511817 , -3.36710357, -1.12610104,\n",
       "          1.9829131 ],\n",
       "        [ 1.46399565, -0.28358385, -2.92408998, -0.70225059,\n",
       "          1.53345295],\n",
       "        ...,\n",
       "        [ 1.45964569, -1.62303603, -2.10083532,  0.78522526,\n",
       "          0.60857052],\n",
       "        [ 0.98134573,  0.11795768, -1.61655869, -0.2982359 ,\n",
       "          0.72860781],\n",
       "        [ 1.87211942, -2.03014332, -2.9094564 ,  0.90545583,\n",
       "          0.89859179]]])</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'>0.7009 0.3478 ... 0.1128 0.1313</div><input id='attrs-d5f8404c-a769-442d-a38f-3b9c207f3804' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5f8404c-a769-442d-a38f-3b9c207f3804' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-443d0e31-1347-48a2-8992-db49c9354f0d' class='xr-var-data-in' type='checkbox'><label for='data-443d0e31-1347-48a2-8992-db49c9354f0d' 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([[0.70090975, 0.34781307, 0.3988521 , ..., 0.09006732, 0.09006732,\n",
       "        0.09006732],\n",
       "       [0.04488868, 0.04896691, 0.04814223, ..., 0.04552586, 0.04958059,\n",
       "        0.04446534],\n",
       "       [0.56074654, 1.42463415, 0.67589924, ..., 0.28199782, 0.25907059,\n",
       "        0.23322822],\n",
       "       [0.21959001, 0.23356949, 0.27390146, ..., 0.12010018, 0.11277224,\n",
       "        0.13131567]])</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'>0.7289 0.2776 ... 0.533 1.005</div><input id='attrs-bf279a76-b3f1-44aa-bb23-33d33707dd5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf279a76-b3f1-44aa-bb23-33d33707dd5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e11d96e-98ae-429b-9dbc-c04dd330d54f' class='xr-var-data-in' type='checkbox'><label for='data-2e11d96e-98ae-429b-9dbc-c04dd330d54f' 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([[[ 7.28919717e-01,  2.77567058e-01, -7.87697729e-02, ...,\n",
       "          2.86121279e-01,  1.31034710e+00,  3.71185270e-02],\n",
       "        [ 2.60352419e-01,  2.66808219e-01,  4.69848718e-01, ...,\n",
       "          1.74932412e+00,  7.75841089e-01,  1.07404277e+00],\n",
       "        [ 1.66573415e-01,  3.03598139e-01,  6.05509856e-01, ...,\n",
       "          4.71592155e-01,  6.02265414e-01,  1.71197116e-03],\n",
       "        ...,\n",
       "        [ 3.77323899e-01,  5.09914842e-01,  5.33673820e-01, ...,\n",
       "          6.52310593e-01,  6.77732648e-01,  1.09823446e+00],\n",
       "        [ 3.77323899e-01,  5.09914842e-01,  5.33673820e-01, ...,\n",
       "          6.52310593e-01,  6.77732648e-01,  1.09823446e+00],\n",
       "        [ 3.77323899e-01,  5.09914842e-01,  5.33673820e-01, ...,\n",
       "          6.52310593e-01,  6.77732648e-01,  1.09823446e+00]],\n",
       "\n",
       "       [[ 3.34287096e-01,  4.00452975e-01,  3.99923091e-01, ...,\n",
       "          5.33886880e-01,  6.19905523e-01,  1.04289140e+00],\n",
       "        [ 3.63849334e-01,  4.02868641e-01,  3.71279614e-01, ...,\n",
       "          5.58594290e-01,  6.46178378e-01,  1.05860877e+00],\n",
       "        [ 2.99417529e-01,  4.03367719e-01,  4.19049991e-01, ...,\n",
       "          5.22966599e-01,  5.92824205e-01,  1.03168802e+00],\n",
       "...\n",
       "        [ 6.82004262e-01,  3.88811698e-01,  1.23127713e-01, ...,\n",
       "          5.82901220e-01,  2.12202291e-01,  5.15716322e-01],\n",
       "        [ 6.11113652e-01,  5.23292485e-01,  4.57755981e-01, ...,\n",
       "          9.82945842e-01,  7.12840550e-01,  8.65737066e-01],\n",
       "        [ 3.19583035e-01,  2.12134858e-01,  1.45117502e-01, ...,\n",
       "          9.16319042e-01,  4.92467674e-01,  8.22746303e-01]],\n",
       "\n",
       "       [[ 6.13563851e-01,  4.39150199e-01,  1.27206687e-01, ...,\n",
       "          7.20217332e-01,  8.73502172e-01,  1.19450541e+00],\n",
       "        [ 7.39656662e-01,  4.72799666e-01,  6.34941484e-02, ...,\n",
       "          3.15121018e-01,  3.98596403e-01,  9.26003605e-01],\n",
       "        [ 6.34321562e-01,  4.46322393e-01,  1.70795934e-01, ...,\n",
       "          6.10366151e-01,  4.04610087e-01,  1.15273707e+00],\n",
       "        ...,\n",
       "        [ 2.46841834e-01,  3.38333776e-01,  4.70868706e-01, ...,\n",
       "          5.64570460e-01,  7.59597168e-01,  9.85634704e-01],\n",
       "        [ 4.75187567e-01,  4.85110184e-01,  4.42240000e-01, ...,\n",
       "          7.36703062e-01,  7.23423521e-01,  9.36206274e-01],\n",
       "        [ 1.80165051e-01,  2.96553153e-01,  4.50409276e-01, ...,\n",
       "          4.08298078e-01,  5.32963684e-01,  1.00461600e+00]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c02dbc2d-d412-46a4-990f-0c8f52b152a3' class='xr-section-summary-in' type='checkbox'  ><label for='section-c02dbc2d-d412-46a4-990f-0c8f52b152a3' 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-0021c677-fe57-4d63-92e6-21fd4d2fbf32' class='xr-index-data-in' type='checkbox'/><label for='index-0021c677-fe57-4d63-92e6-21fd4d2fbf32' 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-38f54d0b-09dd-453b-973b-e2ab9b35ccb5' class='xr-index-data-in' type='checkbox'/><label for='index-38f54d0b-09dd-453b-973b-e2ab9b35ccb5' 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>beta_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b944fda0-41c3-4233-b14d-efa1cf0d26ac' class='xr-index-data-in' type='checkbox'/><label for='index-b944fda0-41c3-4233-b14d-efa1cf0d26ac' 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], dtype=&#x27;int64&#x27;, name=&#x27;beta_dim_0&#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-37626617-8efd-4178-aa50-d3d48c0f729b' class='xr-index-data-in' type='checkbox'/><label for='index-37626617-8efd-4178-aa50-d3d48c0f729b' 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], dtype=&#x27;int64&#x27;, name=&#x27;mu_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56735919-7bfe-45ca-a74c-14cee36b1160' class='xr-section-summary-in' type='checkbox'  checked><label for='section-56735919-7bfe-45ca-a74c-14cee36b1160' class='xr-section-summary' >Attributes: <span>(9)</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>2023-08-11T18:53:39.756266</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></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_predictivee69d65af-2a55-489f-b5f3-fcbe0a8d88e8\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior_predictivee69d65af-2a55-489f-b5f3-fcbe0a8d88e8\" 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",
       "<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",
       "<path d=\"M23 22h-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",
       "<path d=\"M23 18h-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",
       "</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, brain_hat_dim_0: 7)\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",
       "  * brain_hat_dim_0  (brain_hat_dim_0) int64 0 1 2 3 4 5 6\n",
       "Data variables:\n",
       "    brain_hat        (chain, draw, brain_hat_dim_0) float64 -0.6716 ... 1.096\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.902129\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-dde6112f-ae05-42f5-80f9-2a018ebec281' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dde6112f-ae05-42f5-80f9-2a018ebec281' 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'>brain_hat_dim_0</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f7d72f98-ddc6-436b-802b-777cf72cf192' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7d72f98-ddc6-436b-802b-777cf72cf192' 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-7be62245-18c1-49bd-94e7-cff7a6b2979c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7be62245-18c1-49bd-94e7-cff7a6b2979c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bb9da0c-cb9a-4288-871e-e3e62ca8b9bc' class='xr-var-data-in' type='checkbox'><label for='data-7bb9da0c-cb9a-4288-871e-e3e62ca8b9bc' 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-85f3c104-6aac-4bc0-b15b-186c04508d37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85f3c104-6aac-4bc0-b15b-186c04508d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce30cbf1-52cd-4004-bd12-3d80be3db8c2' class='xr-var-data-in' type='checkbox'><label for='data-ce30cbf1-52cd-4004-bd12-3d80be3db8c2' 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'>brain_hat_dim_0</span></div><div class='xr-var-dims'>(brain_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</div><input id='attrs-9d1c1b24-41a7-4493-ae5a-b9768b9af942' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d1c1b24-41a7-4493-ae5a-b9768b9af942' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73848a8c-1130-4ec9-b9c6-2e561de89032' class='xr-var-data-in' type='checkbox'><label for='data-73848a8c-1130-4ec9-b9c6-2e561de89032' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b6d5003-5b1b-42d1-98b8-304ca1c3f8bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b6d5003-5b1b-42d1-98b8-304ca1c3f8bb' 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>brain_hat</span></div><div class='xr-var-dims'>(chain, draw, brain_hat_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6716 0.7404 ... 0.5696 1.096</div><input id='attrs-137afc2e-947a-4619-a0da-7788466bbc5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-137afc2e-947a-4619-a0da-7788466bbc5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe56a9bf-8e21-49e3-97d8-78540f2bf0a5' class='xr-var-data-in' type='checkbox'><label for='data-fe56a9bf-8e21-49e3-97d8-78540f2bf0a5' 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([[[-0.67157625,  0.74044835, -0.05370771, ...,  0.41218331,\n",
       "          0.31817051, -0.56809302],\n",
       "        [ 0.14376081,  0.76836261,  0.67444754, ...,  1.63616778,\n",
       "          0.33656255,  1.07186457],\n",
       "        [ 0.0949888 ,  0.13368191,  0.27441397, ...,  1.14491828,\n",
       "          0.34147127, -0.17442567],\n",
       "        ...,\n",
       "        [ 0.33247667,  0.46912963,  0.50955097, ...,  0.65775759,\n",
       "          0.69695317,  0.8870647 ],\n",
       "        [ 0.38427196,  0.40753803,  0.57867721, ...,  0.64500619,\n",
       "          0.69391179,  1.12743   ],\n",
       "        [ 0.27838681,  0.41336347,  0.69728829, ...,  0.64036479,\n",
       "          0.66338501,  1.01368887]],\n",
       "\n",
       "       [[ 0.38461929,  0.43157113,  0.39087932, ...,  0.52886506,\n",
       "          0.61978389,  1.08284428],\n",
       "        [ 0.27913648,  0.4830043 ,  0.39271807, ...,  0.49234028,\n",
       "          0.71803543,  1.00321182],\n",
       "        [ 0.30125609,  0.39467348,  0.4280089 , ...,  0.33363188,\n",
       "          0.67734479,  1.05794528],\n",
       "...\n",
       "        [ 0.90842314,  0.48742629,  0.50223053, ...,  1.11775084,\n",
       "          0.45654165,  0.62486554],\n",
       "        [ 0.6143996 ,  0.24072102,  0.4114903 , ...,  1.51608438,\n",
       "          0.54100887,  0.80509306],\n",
       "        [ 0.46456858,  0.0884946 ,  0.57334588, ...,  0.41437296,\n",
       "          0.90743546,  0.78007579]],\n",
       "\n",
       "       [[ 0.63935769,  0.06836551, -0.015311  , ...,  1.15595971,\n",
       "          1.05760951,  1.2312529 ],\n",
       "        [ 0.35213352,  0.197653  ,  0.29754935, ...,  0.47394989,\n",
       "          0.62338563,  0.65766756],\n",
       "        [ 1.1548429 ,  0.25337811,  0.02290543, ...,  0.4648817 ,\n",
       "          0.05834947,  1.08793272],\n",
       "        ...,\n",
       "        [ 0.34564091,  0.37021532,  0.44659541, ...,  0.42250087,\n",
       "          0.67991483,  1.07335426],\n",
       "        [ 0.27292318,  0.6111944 ,  0.6369111 , ...,  0.76217779,\n",
       "          1.02446786,  0.83080833],\n",
       "        [ 0.28299893,  0.3752658 ,  0.63134444, ...,  0.64706566,\n",
       "          0.56957333,  1.0960166 ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76d505f4-ba73-46b1-97b1-a875e0fec68d' class='xr-section-summary-in' type='checkbox'  ><label for='section-76d505f4-ba73-46b1-97b1-a875e0fec68d' 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-ff3a17af-dd9d-402d-9cd4-12070b2ef129' class='xr-index-data-in' type='checkbox'/><label for='index-ff3a17af-dd9d-402d-9cd4-12070b2ef129' 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-a5982486-37e7-4df4-ae77-6a1e8a15efcb' class='xr-index-data-in' type='checkbox'/><label for='index-a5982486-37e7-4df4-ae77-6a1e8a15efcb' 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>brain_hat_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0fa91f3-9eb7-406f-a257-e6818cdf3e24' class='xr-index-data-in' type='checkbox'/><label for='index-b0fa91f3-9eb7-406f-a257-e6818cdf3e24' 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], dtype=&#x27;int64&#x27;, name=&#x27;brain_hat_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-78d72848-cc38-46d5-8944-102a966b85f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78d72848-cc38-46d5-8944-102a966b85f7' 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>2023-08-11T18:53:39.902129</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_likelihood52cc0f02-5389-4fa7-8d11-7486cce4ed88\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihood52cc0f02-5389-4fa7-8d11-7486cce4ed88\" 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",
       "<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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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (chain: 4, draw: 1000, brain_dim_0: 7)\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",
       "  * brain_dim_0  (brain_dim_0) int64 0 1 2 3 4 5 6\n",
       "Data variables:\n",
       "    brain        (chain, draw, brain_dim_0) float64 -0.7301 -0.5669 ... 1.111\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.855774\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-d62d4e25-8a58-4af3-b48c-069aebc81ec6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d62d4e25-8a58-4af3-b48c-069aebc81ec6' 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'>brain_dim_0</span>: 7</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d797d02c-9e78-44f8-8a5d-81bb1036b138' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d797d02c-9e78-44f8-8a5d-81bb1036b138' 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-1b323c25-b0d3-412c-b72b-78e39f64e985' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b323c25-b0d3-412c-b72b-78e39f64e985' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bc5b94d-7d8e-4d94-b267-a3a51d39ecc0' class='xr-var-data-in' type='checkbox'><label for='data-8bc5b94d-7d8e-4d94-b267-a3a51d39ecc0' 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-43bcb7fe-1f9c-4710-b40f-77341e3c6084' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43bcb7fe-1f9c-4710-b40f-77341e3c6084' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c74ce9e-cc20-46a4-a792-038c5ac9d0e2' class='xr-var-data-in' type='checkbox'><label for='data-7c74ce9e-cc20-46a4-a792-038c5ac9d0e2' 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'>brain_dim_0</span></div><div class='xr-var-dims'>(brain_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6</div><input id='attrs-fbee4d48-5981-4e7a-a431-3f5730f03dc4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbee4d48-5981-4e7a-a431-3f5730f03dc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffb323b4-84dd-48a3-a540-51457a1f3c1f' class='xr-var-data-in' type='checkbox'><label for='data-ffb323b4-84dd-48a3-a540-51457a1f3c1f' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed329ddf-0a42-4dfa-bb7e-8c84cd069759' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed329ddf-0a42-4dfa-bb7e-8c84cd069759' 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>brain</span></div><div class='xr-var-dims'>(chain, draw, brain_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.7301 -0.5669 ... 0.746 1.111</div><input id='attrs-aab45677-6a17-446f-96d6-afe03a678e1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aab45677-6a17-446f-96d6-afe03a678e1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dab9ff3-7a60-4bd9-9157-0d26d937273c' class='xr-var-data-in' type='checkbox'><label for='data-0dab9ff3-7a60-4bd9-9157-0d26d937273c' 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([[[-7.30068341e-01, -5.66897907e-01, -8.51724842e-01, ...,\n",
       "         -6.38261315e-01, -1.01386095e+00, -1.50717005e+00],\n",
       "        [ 1.20173589e-01,  1.18036313e-01,  1.36024235e-01, ...,\n",
       "         -5.73828167e+00,  6.65951965e-02,  1.14492434e-01],\n",
       "        [-7.81080838e-02, -2.83674445e-03, -7.25588796e-02, ...,\n",
       "         -2.27205375e-02, -5.56370418e-03, -3.13203502e+00],\n",
       "        ...,\n",
       "        [ 1.31591011e+00, -4.01506396e-01,  1.09042304e+00, ...,\n",
       "          9.28784878e-01,  1.42296591e+00,  8.93469865e-01],\n",
       "        [ 1.31591011e+00, -4.01506396e-01,  1.09042304e+00, ...,\n",
       "          9.28784878e-01,  1.42296591e+00,  8.93469865e-01],\n",
       "        [ 1.31591011e+00, -4.01506396e-01,  1.09042304e+00, ...,\n",
       "          9.28784878e-01,  1.42296591e+00,  8.93469865e-01]],\n",
       "\n",
       "       [[ 2.16059186e+00,  1.11555375e+00,  1.47677427e+00, ...,\n",
       "          2.05164561e+00,  2.02605458e+00,  1.72813531e+00],\n",
       "        [ 1.77388096e+00,  1.13191083e+00,  6.93691124e-01, ...,\n",
       "          2.09716635e+00,  2.09746634e+00,  1.38138042e+00],\n",
       "        [ 1.97953314e+00,  1.10081715e+00,  1.86109520e+00, ...,\n",
       "          1.86423472e+00,  1.52318641e+00,  1.89803239e+00],\n",
       "...\n",
       "        [-4.56934270e-01,  3.28585167e-01, -3.38645684e-01, ...,\n",
       "          3.42711356e-01, -8.31827936e-01, -1.12769600e+00],\n",
       "        [-1.80487408e-01,  1.67077330e-01,  4.31570453e-01, ...,\n",
       "         -9.19631222e-01,  3.97617432e-01,  2.97425436e-01],\n",
       "        [ 5.36582065e-01,  3.98456983e-01, -3.36408697e-01, ...,\n",
       "         -6.49730780e-01,  3.22418695e-01,  2.47998479e-01]],\n",
       "\n",
       "       [[-2.69706428e-01,  4.84176855e-01, -5.05797947e-01, ...,\n",
       "          3.20945718e-01,  5.65226674e-02,  2.04763636e-01],\n",
       "        [-1.04473911e+00,  3.60835046e-01, -8.57527069e-01, ...,\n",
       "         -1.03604572e-03, -2.19569449e-02,  4.85153863e-01],\n",
       "        [-2.63922705e-01,  2.93179724e-01, -1.55978078e-01, ...,\n",
       "          3.57093938e-01, -9.68098218e-03,  2.20569887e-01],\n",
       "        ...,\n",
       "        [ 9.91736278e-01,  1.20006130e+00,  1.18983165e+00, ...,\n",
       "          1.19852327e+00,  7.46731041e-01,  1.19333716e+00],\n",
       "        [ 3.70057543e-01,  3.75356947e-01,  1.25860825e+00, ...,\n",
       "         -5.65829205e-03,  1.02278603e+00,  1.10344595e+00],\n",
       "        [ 5.07617798e-01,  1.06876389e+00,  1.11096469e+00, ...,\n",
       "          4.69727833e-01,  7.46047704e-01,  1.11059478e+00]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b028712-1791-4fad-a5f0-0600acafc202' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b028712-1791-4fad-a5f0-0600acafc202' 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-ac13104b-bf87-4a04-9373-4037cd96a51c' class='xr-index-data-in' type='checkbox'/><label for='index-ac13104b-bf87-4a04-9373-4037cd96a51c' 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-3d78711f-9020-4f2b-a06c-8102767c53a8' class='xr-index-data-in' type='checkbox'/><label for='index-3d78711f-9020-4f2b-a06c-8102767c53a8' 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>brain_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c22b69a1-b507-4f2c-a09f-ee26fae446bf' class='xr-index-data-in' type='checkbox'/><label for='index-c22b69a1-b507-4f2c-a09f-ee26fae446bf' 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], dtype=&#x27;int64&#x27;, name=&#x27;brain_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a273295-b0de-48fb-ad7f-56dc1f4edebb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a273295-b0de-48fb-ad7f-56dc1f4edebb' 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>2023-08-11T18:53:39.855774</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_stats3a056c46-15e3-47c1-84df-cc6e7aaf9b71\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats3a056c46-15e3-47c1-84df-cc6e7aaf9b71\" 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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       "</svg>\n",
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       " *\n",
       " */\n",
       "\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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       "  --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",
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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",
       "}\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",
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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",
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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",
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       "  font-weight: 500;\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",
       "}\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",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\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",
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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",
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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",
       "  margin-bottom: 0;\n",
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       "\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",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
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       "}\n",
       "\n",
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       "  grid-column: 3;\n",
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       "  color: var(--xr-font-color2);\n",
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       "\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",
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       "\n",
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       ".xr-var-dims,\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "\n",
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       "\n",
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       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
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       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "  grid-column: 1 / -1;\n",
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       "\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",
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       "\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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  vertical-align: middle;\n",
       "  width: 1em;\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)\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 0.9942 0.9932 1.0 ... 0.7437 0.6009\n",
       "    step_size        (chain, draw) float64 0.02283 0.02283 ... 0.01823 0.01823\n",
       "    tree_depth       (chain, draw) int64 6 7 5 4 8 6 5 4 5 ... 5 6 7 7 5 7 8 7 7\n",
       "    n_steps          (chain, draw) int64 63 159 31 31 255 ... 47 127 255 255 191\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 2.459 5.23 0.8842 ... -7.189 -5.73\n",
       "    lp               (chain, draw) float64 -1.21 0.03107 2.038 ... 10.68 9.605\n",
       "Attributes:\n",
       "    created_at:                 2023-08-11T18:53:39.807300\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</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-cd631a6c-1cc1-409d-8bb6-1c7aa1cc66f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd631a6c-1cc1-409d-8bb6-1c7aa1cc66f6' 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-f35278a2-80e7-4541-be7b-7597c980362c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f35278a2-80e7-4541-be7b-7597c980362c' 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-df2734ab-77c5-4f36-a40a-870f07ac3867' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df2734ab-77c5-4f36-a40a-870f07ac3867' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-553b41bd-6b01-4ca5-85af-1fa6fad7b6f0' class='xr-var-data-in' type='checkbox'><label for='data-553b41bd-6b01-4ca5-85af-1fa6fad7b6f0' 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-38eac7d1-f507-49af-8556-a79ce7e59e44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38eac7d1-f507-49af-8556-a79ce7e59e44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4aae1d-b9b5-4388-826f-ac46c1e9778d' class='xr-var-data-in' type='checkbox'><label for='data-8e4aae1d-b9b5-4388-826f-ac46c1e9778d' 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-0e496432-357a-48d3-9d98-0820f2026d74' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e496432-357a-48d3-9d98-0820f2026d74' 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'>0.9942 0.9932 1.0 ... 0.7437 0.6009</div><input id='attrs-f0dc4848-9c27-47cc-9f17-780ee331dc5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0dc4848-9c27-47cc-9f17-780ee331dc5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52eb09a0-894b-4f7e-8206-68157757a0b7' class='xr-var-data-in' type='checkbox'><label for='data-52eb09a0-894b-4f7e-8206-68157757a0b7' 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([[9.94233707e-01, 9.93165704e-01, 1.00000000e+00, ...,\n",
       "        2.58318986e-01, 1.48519049e-04, 6.71099767e-02],\n",
       "       [7.70774184e-01, 1.80592293e-02, 2.50854737e-01, ...,\n",
       "        2.98825241e-01, 8.26983265e-01, 4.41928941e-01],\n",
       "       [9.85851276e-01, 9.91385247e-01, 9.79344898e-01, ...,\n",
       "        8.60362083e-01, 6.29604725e-01, 6.21876881e-01],\n",
       "       [9.95387414e-01, 9.99909983e-01, 9.99330539e-01, ...,\n",
       "        9.87221552e-01, 7.43663516e-01, 6.00850499e-01]])</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'>0.02283 0.02283 ... 0.01823 0.01823</div><input id='attrs-049d2af0-8bd5-4ce3-9422-ad9461305af8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-049d2af0-8bd5-4ce3-9422-ad9461305af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d259672b-460c-4fee-a26a-8ed45d90cc8f' class='xr-var-data-in' type='checkbox'><label for='data-d259672b-460c-4fee-a26a-8ed45d90cc8f' 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([[0.02283011, 0.02283011, 0.02283011, ..., 0.02283011, 0.02283011,\n",
       "        0.02283011],\n",
       "       [0.00923471, 0.00923471, 0.00923471, ..., 0.00923471, 0.00923471,\n",
       "        0.00923471],\n",
       "       [0.02833399, 0.02833399, 0.02833399, ..., 0.02833399, 0.02833399,\n",
       "        0.02833399],\n",
       "       [0.01823457, 0.01823457, 0.01823457, ..., 0.01823457, 0.01823457,\n",
       "        0.01823457]])</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'>6 7 5 4 8 6 5 4 ... 6 7 7 5 7 8 7 7</div><input id='attrs-abb10f43-a617-4dcb-9e55-94a8c206c94c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abb10f43-a617-4dcb-9e55-94a8c206c94c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb3e4f29-3810-46ec-bb48-e57cb184b661' class='xr-var-data-in' type='checkbox'><label for='data-eb3e4f29-3810-46ec-bb48-e57cb184b661' 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([[6, 7, 5, ..., 1, 1, 1],\n",
       "       [3, 6, 6, ..., 4, 7, 4],\n",
       "       [8, 8, 7, ..., 6, 6, 5],\n",
       "       [6, 5, 6, ..., 8, 7, 7]])</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'>63 159 31 31 ... 127 255 255 191</div><input id='attrs-fb9995a7-1b5c-4f89-8350-3d60bbd9f6a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb9995a7-1b5c-4f89-8350-3d60bbd9f6a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-581d31e8-6fe5-4678-b965-fd10e5ecde4b' class='xr-var-data-in' type='checkbox'><label for='data-581d31e8-6fe5-4678-b965-fd10e5ecde4b' 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([[ 63, 159,  31, ...,   3,   2,   3],\n",
       "       [ 15,  63,  63, ...,  15, 127,  17],\n",
       "       [255, 511, 223, ...,  63,  63,  63],\n",
       "       [ 63,  31, 127, ..., 255, 255, 191]])</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-009fed2d-2ee2-4745-b705-f6d4da2f9600' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-009fed2d-2ee2-4745-b705-f6d4da2f9600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e471ad1-9f95-418e-9a85-7c5544604933' class='xr-var-data-in' type='checkbox'><label for='data-7e471ad1-9f95-418e-9a85-7c5544604933' 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, ...,  True,  True,  True],\n",
       "       [False, False, False, ..., False, False,  True],\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'>2.459 5.23 0.8842 ... -7.189 -5.73</div><input id='attrs-accaf27c-3b1b-48e7-8bf6-da404787b3d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-accaf27c-3b1b-48e7-8bf6-da404787b3d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bb85a1a-2067-4a9a-bd36-3c4ab5374e6f' class='xr-var-data-in' type='checkbox'><label for='data-8bb85a1a-2067-4a9a-bd36-3c4ab5374e6f' 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([[  2.45889561,   5.23014587,   0.8842482 , ...,  -5.24178702,\n",
       "         -7.52119165,  -8.22620056],\n",
       "       [-12.63637198, -12.02113756, -11.70029831, ..., -13.51513333,\n",
       "        -13.30583019,  -3.70330084],\n",
       "       [  3.81284024,  12.21204895,  12.16631997, ...,   0.12223333,\n",
       "          1.31505589,  -4.03915439],\n",
       "       [ -2.14973909,  -2.49191708,  -0.2733382 , ...,  -6.040826  ,\n",
       "         -7.18920473,  -5.72972827]])</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'>-1.21 0.03107 2.038 ... 10.68 9.605</div><input id='attrs-b5993b02-7d11-4528-adb4-d421c1ac2a4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5993b02-7d11-4528-adb4-d421c1ac2a4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-286ae2ba-a74a-45a7-b451-5797ba0afe61' class='xr-var-data-in' type='checkbox'><label for='data-286ae2ba-a74a-45a7-b451-5797ba0afe61' 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([[-1.2099401 ,  0.0310734 ,  2.03763653, ..., 10.35927788,\n",
       "        10.35927788, 10.35927788],\n",
       "       [17.94392058, 16.07488155, 14.50387307, ..., 16.51494071,\n",
       "        14.95817912, 13.73149783],\n",
       "       [-0.84080971, -7.14573667, -4.55199362, ...,  1.82542767,\n",
       "         5.26002971,  6.31266796],\n",
       "       [ 5.66941622,  3.1314764 ,  4.76632921, ..., 10.95431518,\n",
       "        10.68023411,  9.60531483]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e26ec405-da4b-4370-9d1f-7c8d670f467f' class='xr-section-summary-in' type='checkbox'  ><label for='section-e26ec405-da4b-4370-9d1f-7c8d670f467f' 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-e1af61ae-f666-4cb7-b78a-57846f6eb744' class='xr-index-data-in' type='checkbox'/><label for='index-e1af61ae-f666-4cb7-b78a-57846f6eb744' 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-56079561-0df0-4493-93f3-95c69aaa1140' class='xr-index-data-in' type='checkbox'/><label for='index-56079561-0df0-4493-93f3-95c69aaa1140' 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-c44a1112-8cfe-4862-862c-b1e8c2cb8114' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c44a1112-8cfe-4862-862c-b1e8c2cb8114' class='xr-section-summary' >Attributes: <span>(9)</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>2023-08-11T18:53:39.807300</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></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",
       "}\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-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"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5 = az.from_pystan(\n",
    "    posterior=samples_5,\n",
    "    posterior_predictive=\"brain_hat\",\n",
    "    observed_data=data,\n",
    "    log_likelihood={\"brain\": \"log_lik\"},\n",
    ")\n",
    "\n",
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "113ad4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "r2        0.672509\n",
       "r2_std    0.166309\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true = d.brain_std.values\n",
    "y_pred = data5.posterior_predictive.stack(sample=(\"chain\", \"draw\"))[\"brain_hat\"].values.T\n",
    "az.r2_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc098fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rodolpho/Projects/bayesian/BAYES/lib/python3.8/site-packages/arviz/stats/stats.py:803: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "aa = az.loo(samples_5, pointwise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bf98a0df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rodolpho/Projects/bayesian/BAYES/lib/python3.8/site-packages/arviz/stats/stats.py:803: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_elpd({'samples_1':samples_1, 'samples_5': samples_5}, xlabels=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c87d9b9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50019162, 0.61303674, 0.6133973 , 0.44845226, 0.79786415,\n",
       "       2.0166922 , 7.29542366])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaa = data.posterior_predictive.brain_hat[0].values\n",
    "np.log(np.sum(np.exp(aaa.T), 1)) - np.log(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3a46c5",
   "metadata": {},
   "source": [
    "### R Code 7.16\n",
    "\n",
    "Just using Rethinking Packages - pag 213"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d4fdcb",
   "metadata": {},
   "source": [
    "### R Code 7.17\n",
    "\n",
    "Just using Rethinking Packages - Pag 213"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e985fe82",
   "metadata": {},
   "source": [
    "### R Code 7.18\n",
    "\n",
    "Just using Rethinking Packages - pag 214"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775da89d",
   "metadata": {},
   "source": [
    "-------"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}