13_models_with_memory-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ksachdeva/rethinking-tensorflow-probability/blob/master/notebooks/13_models_with_memory.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13. Models with Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import arviz as az\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import namedtuple\n",
    "\n",
    "import random\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow_probability.substrates import jax as tfp\n",
    "tfd = tfp.distributions\n",
    "tfb = tfp.bijectors\n",
    "# to use 'sample_distributions'\n",
    "import tensorflow_probability as xtfp\n",
    "xtfd = xtfp.distributions\n",
    "xtfb = xtfp.bijectors\n",
    "\n",
    "key = tfp.util.SeedStream(529, salt=\"ch-13\")\n",
    "plt.rcParams['figure.figsize'] = [6, 5]\n",
    "\n",
    "# TFP often requires 64-bit precision (float64) for certain computations \n",
    "# to ensure numerical stability and accuracy, e.g., tfd.Possion.\n",
    "# jax.config.update(\"jax_enable_x64\", True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.1 Example: Multilevel tadpoles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.1\n",
    "\n",
    "\n",
    "Reedfrogs dataset is about the tadpole mortality. The objective will be determine the `surv` out of an initial count, `density`.\n",
    "\n",
    "Author explains that within each tank there are things that go unmeasured and these unmeasured factors create variation in survival across tanks.\n",
    "\n",
    "These tanks are an example of **cluster** variable\n",
    "\n",
    "He argues that both of the approaches - \n",
    "    * treat the tanks independetly i.e. each of them have their unique intecepts \n",
    "    * treat them togather \n",
    "\n",
    "have issues. \n",
    "\n",
    "for e.g.\n",
    "\n",
    "    - unique intecepts will imply that we are not using information from other tanks.\n",
    "    - all togather will have the problem ignoring varations in baseline survival \n",
    "    \n",
    "A multilevel model, in which we simultaneously estimate both an intercept for each tank and the variation among tanks, is what we want !\n",
    "\n",
    "This type of a model is called **Varying intercepts** model.\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>density</th>\n",
       "      <th>pred</th>\n",
       "      <th>size</th>\n",
       "      <th>surv</th>\n",
       "      <th>propsurv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>big</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>big</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>big</td>\n",
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       "      <td>0.7</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>big</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>small</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>small</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>small</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>small</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>pred</td>\n",
       "      <td>big</td>\n",
       "      <td>4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>pred</td>\n",
       "      <td>big</td>\n",
       "      <td>9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   density  pred   size  surv  propsurv\n",
       "0       10    no    big     9       0.9\n",
       "1       10    no    big    10       1.0\n",
       "2       10    no    big     7       0.7\n",
       "3       10    no    big    10       1.0\n",
       "4       10    no  small     9       0.9\n",
       "5       10    no  small     9       0.9\n",
       "6       10    no  small    10       1.0\n",
       "7       10    no  small     9       0.9\n",
       "8       10  pred    big     4       0.4\n",
       "9       10  pred    big     9       0.9"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv(\"./data/ReedFrogs.csv\", sep=\";\")\n",
    "d.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.2\n",
    "\n",
    "Our simple model. This will give us 48 different intercepts. This means that it does not use the information available between each tank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_tank = d.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_1(density):\n",
    "\tdef _generator():\n",
    "\t\ta = yield tfd.Sample(tfd.Normal(loc=0.0, scale=1.5), sample_shape=n_tank, name='a')\n",
    "\t\tyield tfd.Binomial(total_count=density, logits=a, name='S')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_1 = model_13_1(d.density.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_1_pin = jd_13_1.experimental_pin(S=d.surv.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_1_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_1_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_1 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_1_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_1._asdict().items()}\n",
    "dict_1 = dict(posterior=post_1_dict)\n",
    "idata_1 = az.from_dict(**dict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>1.700</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.541</td>\n",
       "      <td>2.947</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>2.370</td>\n",
       "      <td>0.872</td>\n",
       "      <td>1.097</td>\n",
       "      <td>3.883</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[2]</th>\n",
       "      <td>0.732</td>\n",
       "      <td>0.642</td>\n",
       "      <td>-0.279</td>\n",
       "      <td>1.776</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.010</td>\n",
       "      <td>3495.0</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[3]</th>\n",
       "      <td>2.432</td>\n",
       "      <td>0.917</td>\n",
       "      <td>1.001</td>\n",
       "      <td>3.814</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>1389.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[4]</th>\n",
       "      <td>1.724</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.637</td>\n",
       "      <td>2.984</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[5]</th>\n",
       "      <td>1.690</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.536</td>\n",
       "      <td>2.895</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.013</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[6]</th>\n",
       "      <td>2.372</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.994</td>\n",
       "      <td>3.745</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[7]</th>\n",
       "      <td>1.694</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.520</td>\n",
       "      <td>2.934</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.013</td>\n",
       "      <td>2091.0</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[8]</th>\n",
       "      <td>-0.367</td>\n",
       "      <td>0.590</td>\n",
       "      <td>-1.319</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2544.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[9]</th>\n",
       "      <td>1.708</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.383</td>\n",
       "      <td>2.749</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[10]</th>\n",
       "      <td>0.742</td>\n",
       "      <td>0.628</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>1.859</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.010</td>\n",
       "      <td>3352.0</td>\n",
       "      <td>1697.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[11]</th>\n",
       "      <td>0.369</td>\n",
       "      <td>0.643</td>\n",
       "      <td>-0.561</td>\n",
       "      <td>1.446</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.012</td>\n",
       "      <td>4459.0</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[12]</th>\n",
       "      <td>0.766</td>\n",
       "      <td>0.645</td>\n",
       "      <td>-0.251</td>\n",
       "      <td>1.798</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.011</td>\n",
       "      <td>3530.0</td>\n",
       "      <td>1589.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[13]</th>\n",
       "      <td>-0.013</td>\n",
       "      <td>0.627</td>\n",
       "      <td>-1.021</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.016</td>\n",
       "      <td>3323.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[14]</th>\n",
       "      <td>1.716</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.300</td>\n",
       "      <td>2.761</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>1416.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[15]</th>\n",
       "      <td>1.711</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.463</td>\n",
       "      <td>2.848</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>1266.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[16]</th>\n",
       "      <td>2.530</td>\n",
       "      <td>0.691</td>\n",
       "      <td>1.415</td>\n",
       "      <td>3.548</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2934.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[17]</th>\n",
       "      <td>2.118</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1.210</td>\n",
       "      <td>3.035</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3204.0</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[18]</th>\n",
       "      <td>1.803</td>\n",
       "      <td>0.533</td>\n",
       "      <td>1.011</td>\n",
       "      <td>2.696</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4292.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[19]</th>\n",
       "      <td>3.087</td>\n",
       "      <td>0.797</td>\n",
       "      <td>1.808</td>\n",
       "      <td>4.330</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[20]</th>\n",
       "      <td>2.138</td>\n",
       "      <td>0.621</td>\n",
       "      <td>1.110</td>\n",
       "      <td>3.055</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.009</td>\n",
       "      <td>3328.0</td>\n",
       "      <td>1363.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[21]</th>\n",
       "      <td>2.156</td>\n",
       "      <td>0.610</td>\n",
       "      <td>1.209</td>\n",
       "      <td>3.123</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3658.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[22]</th>\n",
       "      <td>2.139</td>\n",
       "      <td>0.587</td>\n",
       "      <td>1.133</td>\n",
       "      <td>2.994</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.009</td>\n",
       "      <td>3059.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[23]</th>\n",
       "      <td>1.533</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.810</td>\n",
       "      <td>2.316</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5682.0</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[24]</th>\n",
       "      <td>-1.103</td>\n",
       "      <td>0.441</td>\n",
       "      <td>-1.855</td>\n",
       "      <td>-0.453</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5299.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[25]</th>\n",
       "      <td>0.084</td>\n",
       "      <td>0.397</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4516.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[26]</th>\n",
       "      <td>-1.544</td>\n",
       "      <td>0.507</td>\n",
       "      <td>-2.285</td>\n",
       "      <td>-0.708</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4384.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[27]</th>\n",
       "      <td>-0.559</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-1.170</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>3661.0</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[28]</th>\n",
       "      <td>0.079</td>\n",
       "      <td>0.415</td>\n",
       "      <td>-0.625</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4047.0</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[29]</th>\n",
       "      <td>1.311</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.566</td>\n",
       "      <td>2.074</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5065.0</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[30]</th>\n",
       "      <td>-0.725</td>\n",
       "      <td>0.416</td>\n",
       "      <td>-1.360</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4758.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[31]</th>\n",
       "      <td>-0.391</td>\n",
       "      <td>0.403</td>\n",
       "      <td>-1.006</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>3721.0</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[32]</th>\n",
       "      <td>2.840</td>\n",
       "      <td>0.658</td>\n",
       "      <td>1.748</td>\n",
       "      <td>3.800</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.010</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[33]</th>\n",
       "      <td>2.472</td>\n",
       "      <td>0.586</td>\n",
       "      <td>1.540</td>\n",
       "      <td>3.417</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.009</td>\n",
       "      <td>3243.0</td>\n",
       "      <td>1376.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[34]</th>\n",
       "      <td>2.455</td>\n",
       "      <td>0.566</td>\n",
       "      <td>1.581</td>\n",
       "      <td>3.389</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3767.0</td>\n",
       "      <td>1346.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[35]</th>\n",
       "      <td>1.908</td>\n",
       "      <td>0.474</td>\n",
       "      <td>1.109</td>\n",
       "      <td>2.611</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4791.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[36]</th>\n",
       "      <td>1.907</td>\n",
       "      <td>0.479</td>\n",
       "      <td>1.141</td>\n",
       "      <td>2.650</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4761.0</td>\n",
       "      <td>1252.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[37]</th>\n",
       "      <td>3.379</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2.210</td>\n",
       "      <td>4.629</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[38]</th>\n",
       "      <td>2.465</td>\n",
       "      <td>0.584</td>\n",
       "      <td>1.488</td>\n",
       "      <td>3.298</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.009</td>\n",
       "      <td>4450.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[39]</th>\n",
       "      <td>2.154</td>\n",
       "      <td>0.520</td>\n",
       "      <td>1.341</td>\n",
       "      <td>2.960</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[40]</th>\n",
       "      <td>-1.901</td>\n",
       "      <td>0.494</td>\n",
       "      <td>-2.655</td>\n",
       "      <td>-1.127</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>6533.0</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[41]</th>\n",
       "      <td>-0.630</td>\n",
       "      <td>0.345</td>\n",
       "      <td>-1.158</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2545.0</td>\n",
       "      <td>1404.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[42]</th>\n",
       "      <td>-0.506</td>\n",
       "      <td>0.335</td>\n",
       "      <td>-1.064</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2970.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[43]</th>\n",
       "      <td>-0.395</td>\n",
       "      <td>0.341</td>\n",
       "      <td>-0.892</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2549.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[44]</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.354</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[45]</th>\n",
       "      <td>-0.615</td>\n",
       "      <td>0.338</td>\n",
       "      <td>-1.168</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[46]</th>\n",
       "      <td>1.901</td>\n",
       "      <td>0.461</td>\n",
       "      <td>1.126</td>\n",
       "      <td>2.592</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4412.0</td>\n",
       "      <td>1443.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[47]</th>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.319</td>\n",
       "      <td>-0.566</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.007</td>\n",
       "      <td>2577.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a[0]   1.700  0.760     0.541      2.947      0.017    0.014    1988.0   \n",
       "a[1]   2.370  0.872     1.097      3.883      0.024    0.018    1311.0   \n",
       "a[2]   0.732  0.642    -0.279      1.776      0.011    0.010    3495.0   \n",
       "a[3]   2.432  0.917     1.001      3.814      0.024    0.018    1585.0   \n",
       "a[4]   1.724  0.752     0.637      2.984      0.018    0.014    1827.0   \n",
       "a[5]   1.690  0.745     0.536      2.895      0.017    0.013    2001.0   \n",
       "a[6]   2.372  0.873     0.994      3.745      0.023    0.017    1560.0   \n",
       "a[7]   1.694  0.753     0.520      2.934      0.017    0.013    2091.0   \n",
       "a[8]  -0.367  0.590    -1.319      0.520      0.012    0.011    2544.0   \n",
       "a[9]   1.708  0.741     0.383      2.749      0.017    0.014    1984.0   \n",
       "a[10]  0.742  0.628    -0.139      1.859      0.011    0.010    3352.0   \n",
       "a[11]  0.369  0.643    -0.561      1.446      0.010    0.012    4459.0   \n",
       "a[12]  0.766  0.645    -0.251      1.798      0.011    0.011    3530.0   \n",
       "a[13] -0.013  0.627    -1.021      0.991      0.011    0.016    3323.0   \n",
       "a[14]  1.716  0.769     0.300      2.761      0.018    0.014    1951.0   \n",
       "a[15]  1.711  0.761     0.463      2.848      0.018    0.015    1971.0   \n",
       "a[16]  2.530  0.691     1.415      3.548      0.013    0.011    2934.0   \n",
       "a[17]  2.118  0.588     1.210      3.035      0.011    0.008    3204.0   \n",
       "a[18]  1.803  0.533     1.011      2.696      0.008    0.007    4292.0   \n",
       "a[19]  3.087  0.797     1.808      4.330      0.020    0.015    1755.0   \n",
       "a[20]  2.138  0.621     1.110      3.055      0.011    0.009    3328.0   \n",
       "a[21]  2.156  0.610     1.209      3.123      0.010    0.008    3658.0   \n",
       "a[22]  2.139  0.587     1.133      2.994      0.011    0.009    3059.0   \n",
       "a[23]  1.533  0.487     0.810      2.316      0.007    0.006    5682.0   \n",
       "a[24] -1.103  0.441    -1.855     -0.453      0.006    0.006    5299.0   \n",
       "a[25]  0.084  0.397    -0.545      0.716      0.006    0.009    4516.0   \n",
       "a[26] -1.544  0.507    -2.285     -0.708      0.008    0.007    4384.0   \n",
       "a[27] -0.559  0.400    -1.170      0.104      0.007    0.006    3661.0   \n",
       "a[28]  0.079  0.415    -0.625      0.691      0.007    0.009    4047.0   \n",
       "a[29]  1.311  0.471     0.566      2.074      0.007    0.006    5065.0   \n",
       "a[30] -0.725  0.416    -1.360     -0.010      0.006    0.006    4758.0   \n",
       "a[31] -0.391  0.403    -1.006      0.254      0.007    0.007    3721.0   \n",
       "a[32]  2.840  0.658     1.748      3.800      0.013    0.010    2711.0   \n",
       "a[33]  2.472  0.586     1.540      3.417      0.011    0.009    3243.0   \n",
       "a[34]  2.455  0.566     1.581      3.389      0.010    0.008    3767.0   \n",
       "a[35]  1.908  0.474     1.109      2.611      0.007    0.006    4791.0   \n",
       "a[36]  1.907  0.479     1.141      2.650      0.007    0.006    4761.0   \n",
       "a[37]  3.379  0.786     2.210      4.629      0.019    0.014    1915.0   \n",
       "a[38]  2.465  0.584     1.488      3.298      0.010    0.009    4450.0   \n",
       "a[39]  2.154  0.520     1.341      2.960      0.007    0.006    5725.0   \n",
       "a[40] -1.901  0.494    -2.655     -1.127      0.006    0.005    6533.0   \n",
       "a[41] -0.630  0.345    -1.158     -0.070      0.007    0.005    2545.0   \n",
       "a[42] -0.506  0.335    -1.064      0.020      0.006    0.005    2970.0   \n",
       "a[43] -0.395  0.341    -0.892      0.199      0.007    0.006    2549.0   \n",
       "a[44]  0.500  0.354    -0.037      1.080      0.007    0.006    2559.0   \n",
       "a[45] -0.615  0.338    -1.168     -0.079      0.006    0.005    2977.0   \n",
       "a[46]  1.901  0.461     1.126      2.592      0.007    0.006    4412.0   \n",
       "a[47] -0.061  0.319    -0.566      0.415      0.006    0.007    2577.0   \n",
       "\n",
       "       ess_tail  r_hat  \n",
       "a[0]     1444.0   1.00  \n",
       "a[1]     1277.0   1.00  \n",
       "a[2]     1484.0   1.00  \n",
       "a[3]     1389.0   1.00  \n",
       "a[4]     1387.0   1.00  \n",
       "a[5]     1474.0   1.00  \n",
       "a[6]     1210.0   1.00  \n",
       "a[7]     1298.0   1.00  \n",
       "a[8]     1442.0   1.00  \n",
       "a[9]     1402.0   1.00  \n",
       "a[10]    1697.0   1.00  \n",
       "a[11]    1655.0   1.00  \n",
       "a[12]    1589.0   1.00  \n",
       "a[13]    1494.0   1.01  \n",
       "a[14]    1416.0   1.00  \n",
       "a[15]    1266.0   1.00  \n",
       "a[16]    1541.0   1.00  \n",
       "a[17]    1394.0   1.00  \n",
       "a[18]    1202.0   1.01  \n",
       "a[19]    1399.0   1.00  \n",
       "a[20]    1363.0   1.00  \n",
       "a[21]    1480.0   1.00  \n",
       "a[22]    1142.0   1.00  \n",
       "a[23]    1413.0   1.00  \n",
       "a[24]    1206.0   1.01  \n",
       "a[25]    1436.0   1.00  \n",
       "a[26]    1442.0   1.00  \n",
       "a[27]    1431.0   1.00  \n",
       "a[28]    1742.0   1.00  \n",
       "a[29]    1431.0   1.00  \n",
       "a[30]    1436.0   1.00  \n",
       "a[31]    1465.0   1.00  \n",
       "a[32]    1316.0   1.00  \n",
       "a[33]    1376.0   1.00  \n",
       "a[34]    1346.0   1.00  \n",
       "a[35]    1348.0   1.00  \n",
       "a[36]    1252.0   1.00  \n",
       "a[37]    1387.0   1.01  \n",
       "a[38]    1243.0   1.00  \n",
       "a[39]    1551.0   1.00  \n",
       "a[40]    1431.0   1.00  \n",
       "a[41]    1404.0   1.00  \n",
       "a[42]    1516.0   1.00  \n",
       "a[43]    1275.0   1.00  \n",
       "a[44]    1387.0   1.00  \n",
       "a[45]    1407.0   1.00  \n",
       "a[46]    1443.0   1.00  \n",
       "a[47]    1348.0   1.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_1, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.3\n",
    "\n",
    "We now build a multilevel model, which adaptively pools information across tanks.\n",
    "\n",
    "In order to do so, we must make the prior for the parameter **alpha** a function of some new parameters.\n",
    "\n",
    "Prior itself has priors !\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_2(density):\n",
    "\tdef _generator():\n",
    "\t\ta_bar = yield tfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma = yield tfd.Exponential(rate=1.0, name='sigma')\n",
    "\t\ta = yield tfd.Sample(\n",
    "\t\t\t\ttfd.Normal(loc=a_bar, scale=sigma), sample_shape=n_tank, name='a')\n",
    "\t\tyield tfd.Binomial(total_count=density, logits=a, name='S')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_2 = model_13_2(d.density.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_2_pin = jd_13_2.experimental_pin(S=d.surv.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_2_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Exp(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_2_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_2 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_2_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_2._asdict().items()}\n",
    "dict_2 = dict(posterior=post_2_dict)\n",
    "idata_2 = az.from_dict(**dict_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>2.121</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.768</td>\n",
       "      <td>3.528</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.024</td>\n",
       "      <td>802.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>3.083</td>\n",
       "      <td>1.131</td>\n",
       "      <td>1.339</td>\n",
       "      <td>4.837</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.032</td>\n",
       "      <td>644.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[2]</th>\n",
       "      <td>1.018</td>\n",
       "      <td>0.701</td>\n",
       "      <td>-0.092</td>\n",
       "      <td>2.089</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1641.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[3]</th>\n",
       "      <td>3.189</td>\n",
       "      <td>1.158</td>\n",
       "      <td>1.241</td>\n",
       "      <td>4.810</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.034</td>\n",
       "      <td>731.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[4]</th>\n",
       "      <td>2.153</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.817</td>\n",
       "      <td>3.449</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.022</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[5]</th>\n",
       "      <td>2.172</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.818</td>\n",
       "      <td>3.611</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[6]</th>\n",
       "      <td>3.155</td>\n",
       "      <td>1.143</td>\n",
       "      <td>1.528</td>\n",
       "      <td>5.057</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.034</td>\n",
       "      <td>677.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[7]</th>\n",
       "      <td>2.102</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.748</td>\n",
       "      <td>3.470</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[8]</th>\n",
       "      <td>-0.169</td>\n",
       "      <td>0.608</td>\n",
       "      <td>-1.125</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.012</td>\n",
       "      <td>2306.0</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[9]</th>\n",
       "      <td>2.108</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.797</td>\n",
       "      <td>3.339</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.019</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>1031.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[10]</th>\n",
       "      <td>0.985</td>\n",
       "      <td>0.674</td>\n",
       "      <td>-0.073</td>\n",
       "      <td>1.980</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1773.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[11]</th>\n",
       "      <td>0.576</td>\n",
       "      <td>0.656</td>\n",
       "      <td>-0.464</td>\n",
       "      <td>1.621</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.013</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[12]</th>\n",
       "      <td>0.993</td>\n",
       "      <td>0.681</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>2.086</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>1204.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[13]</th>\n",
       "      <td>0.230</td>\n",
       "      <td>0.603</td>\n",
       "      <td>-0.697</td>\n",
       "      <td>1.217</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.014</td>\n",
       "      <td>2145.0</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[14]</th>\n",
       "      <td>2.124</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.851</td>\n",
       "      <td>3.465</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1143.0</td>\n",
       "      <td>890.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[15]</th>\n",
       "      <td>2.148</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.816</td>\n",
       "      <td>3.614</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.023</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>885.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[16]</th>\n",
       "      <td>2.939</td>\n",
       "      <td>0.822</td>\n",
       "      <td>1.632</td>\n",
       "      <td>4.189</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[17]</th>\n",
       "      <td>2.419</td>\n",
       "      <td>0.668</td>\n",
       "      <td>1.313</td>\n",
       "      <td>3.377</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1847.0</td>\n",
       "      <td>1308.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[18]</th>\n",
       "      <td>2.022</td>\n",
       "      <td>0.561</td>\n",
       "      <td>1.116</td>\n",
       "      <td>2.882</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.009</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[19]</th>\n",
       "      <td>3.625</td>\n",
       "      <td>0.968</td>\n",
       "      <td>1.926</td>\n",
       "      <td>4.969</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.024</td>\n",
       "      <td>922.0</td>\n",
       "      <td>903.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[20]</th>\n",
       "      <td>2.427</td>\n",
       "      <td>0.684</td>\n",
       "      <td>1.366</td>\n",
       "      <td>3.467</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1711.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[21]</th>\n",
       "      <td>2.425</td>\n",
       "      <td>0.658</td>\n",
       "      <td>1.366</td>\n",
       "      <td>3.423</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1912.0</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[22]</th>\n",
       "      <td>2.394</td>\n",
       "      <td>0.668</td>\n",
       "      <td>1.393</td>\n",
       "      <td>3.476</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[23]</th>\n",
       "      <td>1.687</td>\n",
       "      <td>0.534</td>\n",
       "      <td>0.905</td>\n",
       "      <td>2.549</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2797.0</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[24]</th>\n",
       "      <td>-1.001</td>\n",
       "      <td>0.469</td>\n",
       "      <td>-1.754</td>\n",
       "      <td>-0.247</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>3814.0</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[25]</th>\n",
       "      <td>0.159</td>\n",
       "      <td>0.401</td>\n",
       "      <td>-0.448</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.009</td>\n",
       "      <td>3381.0</td>\n",
       "      <td>1349.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[26]</th>\n",
       "      <td>-1.417</td>\n",
       "      <td>0.487</td>\n",
       "      <td>-2.133</td>\n",
       "      <td>-0.611</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2591.0</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[27]</th>\n",
       "      <td>-0.470</td>\n",
       "      <td>0.394</td>\n",
       "      <td>-1.057</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[28]</th>\n",
       "      <td>0.164</td>\n",
       "      <td>0.378</td>\n",
       "      <td>-0.444</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[29]</th>\n",
       "      <td>1.451</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.647</td>\n",
       "      <td>2.259</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>1269.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[30]</th>\n",
       "      <td>-0.643</td>\n",
       "      <td>0.395</td>\n",
       "      <td>-1.230</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4013.0</td>\n",
       "      <td>1459.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[31]</th>\n",
       "      <td>-0.307</td>\n",
       "      <td>0.392</td>\n",
       "      <td>-0.901</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4841.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[32]</th>\n",
       "      <td>3.208</td>\n",
       "      <td>0.804</td>\n",
       "      <td>1.879</td>\n",
       "      <td>4.348</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[33]</th>\n",
       "      <td>2.692</td>\n",
       "      <td>0.653</td>\n",
       "      <td>1.672</td>\n",
       "      <td>3.737</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1882.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[34]</th>\n",
       "      <td>2.707</td>\n",
       "      <td>0.630</td>\n",
       "      <td>1.698</td>\n",
       "      <td>3.665</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.011</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[35]</th>\n",
       "      <td>2.053</td>\n",
       "      <td>0.505</td>\n",
       "      <td>1.213</td>\n",
       "      <td>2.788</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2468.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[36]</th>\n",
       "      <td>2.061</td>\n",
       "      <td>0.532</td>\n",
       "      <td>1.207</td>\n",
       "      <td>2.883</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2983.0</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[37]</th>\n",
       "      <td>3.926</td>\n",
       "      <td>0.997</td>\n",
       "      <td>2.354</td>\n",
       "      <td>5.349</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.026</td>\n",
       "      <td>855.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[38]</th>\n",
       "      <td>2.718</td>\n",
       "      <td>0.649</td>\n",
       "      <td>1.783</td>\n",
       "      <td>3.783</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[39]</th>\n",
       "      <td>2.368</td>\n",
       "      <td>0.589</td>\n",
       "      <td>1.388</td>\n",
       "      <td>3.252</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[40]</th>\n",
       "      <td>-1.814</td>\n",
       "      <td>0.485</td>\n",
       "      <td>-2.532</td>\n",
       "      <td>-1.043</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[41]</th>\n",
       "      <td>-0.560</td>\n",
       "      <td>0.345</td>\n",
       "      <td>-1.110</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[42]</th>\n",
       "      <td>-0.447</td>\n",
       "      <td>0.340</td>\n",
       "      <td>-0.977</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3295.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[43]</th>\n",
       "      <td>-0.332</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.846</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>3327.0</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[44]</th>\n",
       "      <td>0.575</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.057</td>\n",
       "      <td>1.122</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3930.0</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[45]</th>\n",
       "      <td>-0.578</td>\n",
       "      <td>0.350</td>\n",
       "      <td>-1.089</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4059.0</td>\n",
       "      <td>1506.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[46]</th>\n",
       "      <td>2.059</td>\n",
       "      <td>0.498</td>\n",
       "      <td>1.279</td>\n",
       "      <td>2.838</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2886.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[47]</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.330</td>\n",
       "      <td>-0.536</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.008</td>\n",
       "      <td>3029.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_bar</th>\n",
       "      <td>1.355</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.969</td>\n",
       "      <td>1.769</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1522.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.624</td>\n",
       "      <td>0.215</td>\n",
       "      <td>1.255</td>\n",
       "      <td>1.921</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a[0]   2.121  0.881     0.768      3.528      0.032    0.024     802.0   \n",
       "a[1]   3.083  1.131     1.339      4.837      0.045    0.032     644.0   \n",
       "a[2]   1.018  0.701    -0.092      2.089      0.018    0.015    1641.0   \n",
       "a[3]   3.189  1.158     1.241      4.810      0.045    0.034     731.0   \n",
       "a[4]   2.153  0.877     0.817      3.449      0.028    0.022    1105.0   \n",
       "a[5]   2.172  0.901     0.818      3.611      0.027    0.020    1110.0   \n",
       "a[6]   3.155  1.143     1.528      5.057      0.046    0.034     677.0   \n",
       "a[7]   2.102  0.878     0.748      3.470      0.027    0.020    1209.0   \n",
       "a[8]  -0.169  0.608    -1.125      0.807      0.013    0.012    2306.0   \n",
       "a[9]   2.108  0.817     0.797      3.339      0.025    0.019    1131.0   \n",
       "a[10]  0.985  0.674    -0.073      1.980      0.016    0.012    1773.0   \n",
       "a[11]  0.576  0.656    -0.464      1.621      0.014    0.013    2280.0   \n",
       "a[12]  0.993  0.681    -0.045      2.086      0.016    0.014    1935.0   \n",
       "a[13]  0.230  0.603    -0.697      1.217      0.013    0.014    2145.0   \n",
       "a[14]  2.124  0.843     0.851      3.465      0.026    0.020    1143.0   \n",
       "a[15]  2.148  0.902     0.816      3.614      0.028    0.023    1121.0   \n",
       "a[16]  2.939  0.822     1.632      4.189      0.024    0.018    1239.0   \n",
       "a[17]  2.419  0.668     1.313      3.377      0.016    0.012    1847.0   \n",
       "a[18]  2.022  0.561     1.116      2.882      0.011    0.009    2555.0   \n",
       "a[19]  3.625  0.968     1.926      4.969      0.033    0.024     922.0   \n",
       "a[20]  2.427  0.684     1.366      3.467      0.018    0.014    1711.0   \n",
       "a[21]  2.425  0.658     1.366      3.423      0.016    0.012    1912.0   \n",
       "a[22]  2.394  0.668     1.393      3.476      0.017    0.013    1688.0   \n",
       "a[23]  1.687  0.534     0.905      2.549      0.010    0.008    2797.0   \n",
       "a[24] -1.001  0.469    -1.754     -0.247      0.008    0.007    3814.0   \n",
       "a[25]  0.159  0.401    -0.448      0.826      0.007    0.009    3381.0   \n",
       "a[26] -1.417  0.487    -2.133     -0.611      0.010    0.008    2591.0   \n",
       "a[27] -0.470  0.394    -1.057      0.175      0.007    0.008    3038.0   \n",
       "a[28]  0.164  0.378    -0.444      0.738      0.006    0.008    3436.0   \n",
       "a[29]  1.451  0.505     0.647      2.259      0.009    0.008    3250.0   \n",
       "a[30] -0.643  0.395    -1.230      0.034      0.006    0.006    4013.0   \n",
       "a[31] -0.307  0.392    -0.901      0.336      0.006    0.007    4841.0   \n",
       "a[32]  3.208  0.804     1.879      4.348      0.023    0.017    1367.0   \n",
       "a[33]  2.692  0.653     1.672      3.737      0.016    0.012    1882.0   \n",
       "a[34]  2.707  0.630     1.698      3.665      0.015    0.011    1984.0   \n",
       "a[35]  2.053  0.505     1.213      2.788      0.011    0.008    2468.0   \n",
       "a[36]  2.061  0.532     1.207      2.883      0.010    0.008    2983.0   \n",
       "a[37]  3.926  0.997     2.354      5.349      0.035    0.026     855.0   \n",
       "a[38]  2.718  0.649     1.783      3.783      0.016    0.012    1990.0   \n",
       "a[39]  2.368  0.589     1.388      3.252      0.014    0.011    1996.0   \n",
       "a[40] -1.814  0.485    -2.532     -1.043      0.010    0.008    2630.0   \n",
       "a[41] -0.560  0.345    -1.110     -0.011      0.006    0.005    3753.0   \n",
       "a[42] -0.447  0.340    -0.977      0.103      0.006    0.005    3295.0   \n",
       "a[43] -0.332  0.333    -0.846      0.228      0.006    0.006    3327.0   \n",
       "a[44]  0.575  0.339     0.057      1.122      0.005    0.005    3930.0   \n",
       "a[45] -0.578  0.350    -1.089     -0.016      0.006    0.005    4059.0   \n",
       "a[46]  2.059  0.498     1.279      2.838      0.010    0.008    2886.0   \n",
       "a[47]  0.004  0.330    -0.536      0.519      0.006    0.008    3029.0   \n",
       "a_bar  1.355  0.255     0.969      1.769      0.007    0.005    1522.0   \n",
       "sigma  1.624  0.215     1.255      1.921      0.007    0.005    1015.0   \n",
       "\n",
       "       ess_tail  r_hat  \n",
       "a[0]      645.0   1.01  \n",
       "a[1]      741.0   1.01  \n",
       "a[2]     1160.0   1.00  \n",
       "a[3]      716.0   1.00  \n",
       "a[4]      671.0   1.00  \n",
       "a[5]     1217.0   1.00  \n",
       "a[6]      660.0   1.00  \n",
       "a[7]      962.0   1.00  \n",
       "a[8]     1461.0   1.00  \n",
       "a[9]     1031.0   1.00  \n",
       "a[10]    1079.0   1.00  \n",
       "a[11]    1305.0   1.00  \n",
       "a[12]    1204.0   1.00  \n",
       "a[13]    1149.0   1.00  \n",
       "a[14]     890.0   1.00  \n",
       "a[15]     885.0   1.00  \n",
       "a[16]    1002.0   1.00  \n",
       "a[17]    1308.0   1.00  \n",
       "a[18]    1634.0   1.00  \n",
       "a[19]     903.0   1.00  \n",
       "a[20]    1048.0   1.00  \n",
       "a[21]    1055.0   1.00  \n",
       "a[22]    1202.0   1.00  \n",
       "a[23]    1522.0   1.00  \n",
       "a[24]    1570.0   1.00  \n",
       "a[25]    1349.0   1.00  \n",
       "a[26]    1418.0   1.01  \n",
       "a[27]    1280.0   1.00  \n",
       "a[28]    1496.0   1.00  \n",
       "a[29]    1269.0   1.00  \n",
       "a[30]    1459.0   1.00  \n",
       "a[31]    1551.0   1.00  \n",
       "a[32]     987.0   1.00  \n",
       "a[33]    1296.0   1.00  \n",
       "a[34]    1095.0   1.00  \n",
       "a[35]    1296.0   1.00  \n",
       "a[36]    1101.0   1.00  \n",
       "a[37]     970.0   1.00  \n",
       "a[38]    1285.0   1.00  \n",
       "a[39]    1096.0   1.00  \n",
       "a[40]    1230.0   1.00  \n",
       "a[41]    1337.0   1.00  \n",
       "a[42]    1218.0   1.00  \n",
       "a[43]    1329.0   1.01  \n",
       "a[44]    1261.0   1.00  \n",
       "a[45]    1506.0   1.01  \n",
       "a[46]    1337.0   1.00  \n",
       "a[47]    1396.0   1.00  \n",
       "a_bar    1275.0   1.00  \n",
       "sigma    1354.0   1.00  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_2, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_1x(density):\n",
    "\tdef _generator():\n",
    "\t\ta = yield xtfd.Sample(xtfd.Normal(loc=0.0, scale=1.5), sample_shape=n_tank, name='a')\n",
    "\t\tyield xtfd.Binomial(total_count=density, logits=a, name='S')\n",
    "\n",
    "\treturn xtfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_1x = model_13_1x(d.density.values.astype(np.float32))\n",
    "ds_1, _ = jd_1x.sample_distributions(value=post_1)\n",
    "ll_1 = ds_1[-1].log_prob(d.surv.values)\n",
    "ll_1 = np.moveaxis(ll_1, 0, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_1.update(log_likelihood={'ll': ll_1},\n",
    "              dims={\"ll\": [\"obs\"]},\n",
    "              coords={\"obs\": np.arange(len(d.surv.values))})\n",
    "idata_1 = az.from_dict(**dict_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_2x(density):\n",
    "\tdef _generator():\n",
    "\t\ta_bar = yield xtfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma = yield xtfd.Exponential(rate=1.0, name='sigma')\n",
    "\t\ta = yield xtfd.Sample(\n",
    "\t\t\t\txtfd.Normal(loc=a_bar, scale=sigma), sample_shape=n_tank, name='a')\n",
    "\t\tyield xtfd.Binomial(total_count=density, logits=a, name='S')\n",
    "\n",
    "\treturn xtfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_2x = model_13_2x(d.density.values.astype(np.float32))\n",
    "ds_2, _ = jd_2x.sample_distributions(value=post_2)\n",
    "ll_2 = ds_2[-1].log_prob(d.surv.values)\n",
    "ll_2 = np.moveaxis(ll_2, 0, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_2.update(log_likelihood={'ll': ll_2},\n",
    "              dims={\"ll\": [\"obs\"]},\n",
    "              coords={\"obs\": np.arange(len(d.surv.values))})\n",
    "idata_2 = az.from_dict(**dict_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jun/miniforge3/envs/dl/lib/python3.10/site-packages/arviz/stats/stats.py:1653: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
      "See http://arxiv.org/abs/1507.04544 for details\n",
      "  warnings.warn(\n",
      "/Users/jun/miniforge3/envs/dl/lib/python3.10/site-packages/arviz/stats/stats.py:1653: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. \n",
      "See http://arxiv.org/abs/1507.04544 for details\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>elpd_waic</th>\n",
       "      <th>p_waic</th>\n",
       "      <th>elpd_diff</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m13.2</th>\n",
       "      <td>0</td>\n",
       "      <td>199.775282</td>\n",
       "      <td>20.836952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.146740</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>True</td>\n",
       "      <td>deviance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m13.1</th>\n",
       "      <td>1</td>\n",
       "      <td>214.425759</td>\n",
       "      <td>25.478062</td>\n",
       "      <td>14.650477</td>\n",
       "      <td>4.707346e-14</td>\n",
       "      <td>4.417992</td>\n",
       "      <td>3.88301</td>\n",
       "      <td>True</td>\n",
       "      <td>deviance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank   elpd_waic     p_waic  elpd_diff        weight        se  \\\n",
       "m13.2     0  199.775282  20.836952   0.000000  1.000000e+00  7.146740   \n",
       "m13.1     1  214.425759  25.478062  14.650477  4.707346e-14  4.417992   \n",
       "\n",
       "           dse  warning     scale  \n",
       "m13.2  0.00000     True  deviance  \n",
       "m13.1  3.88301     True  deviance  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.compare({\"m13.1\": idata_1, \"m13.2\": idata_2}, ic='waic', scale=\"deviance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array(0.7898544, dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_a_bar = post_2_dict[\"a_bar\"][0]\n",
    "jnp.mean(jax.nn.sigmoid(sample_a_bar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute median intercept for each tank\n",
    "# also transform to probability with logistic\n",
    "\n",
    "sample_alpha = post_2_dict['a'][0]\n",
    "propsurv_est = jax.nn.sigmoid(jnp.mean(sample_alpha, 0))\n",
    "\n",
    "# display raw proportions surviving in each tank\n",
    "plt.plot(np.arange(1, 49), d.propsurv, \"o\", alpha=0.5, zorder=3)\n",
    "plt.gca().set(ylim=(-0.05, 1.05), xlabel=\"tank\", ylabel=\"proportion survival\")\n",
    "plt.gca().set(xticks=[1, 16, 32, 48], xticklabels=[1, 16, 32, 48])\n",
    "\n",
    "# overlay posterior means\n",
    "plt.plot(np.arange(1, 49), propsurv_est, \"ko\", mfc=\"w\")\n",
    "\n",
    "sample_a_bar = post_2_dict[\"a_bar\"][0]\n",
    "\n",
    "# mark posterior mean probability across tanks\n",
    "plt.gca().axhline(\n",
    "    y=jnp.mean(jax.nn.sigmoid(sample_a_bar)), c=\"k\", ls=\"--\", lw=1)\n",
    "\n",
    "# draw vertical dividers between tank densities\n",
    "plt.gca().axvline(x=16.5, c=\"k\", lw=0.5)\n",
    "plt.gca().axvline(x=32.5, c=\"k\", lw=0.5)\n",
    "plt.annotate(\"small tanks\", (8, 0), ha=\"center\")\n",
    "plt.annotate(\"medium tanks\", (16 + 8, 0), ha=\"center\")\n",
    "plt.annotate(\"large tanks\", (32 + 8, 0), ha=\"center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is how to read above plot -\n",
    "\n",
    "* The dashed line locates the average proportion of survivors across all tanks\n",
    "* The vertical lines divide tanks with different initial densities of tadpoles: small tanks (10 tad- poles), medium tanks (25), and large tanks (35).\n",
    "* Empirical proportions of survivors in each tadpole tank, shown by the filled blue points, plotted with the 48 per-tank parameters from the multilevel model, shown by the black circles\n",
    "\n",
    "In every tank, the posterior mean from the multilevel model is closer to the dashed line than the empir- ical proportion is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_sigma = post_2_dict[\"sigma\"][0]\n",
    "\n",
    "# show first 100 populations in the posterior\n",
    "plt.subplot(xlim=(-3, 4), ylim=(0, 0.35), xlabel=\"log-odds survive\", ylabel=\"Density\")\n",
    "\n",
    "x = np.linspace(-3, 4, 101)\n",
    "for i in range(100):\n",
    "    plt.plot(\n",
    "        x,\n",
    "        tfd.Normal(sample_a_bar[i], sample_sigma[i]).prob(x),\n",
    "        \"k\",\n",
    "        alpha=0.2,\n",
    "    )\n",
    "plt.show()\n",
    "\n",
    "# sample 8000 imaginary tanks from the posterior distribution\n",
    "idxs = jax.random.randint(key=key(), shape=8000, minval=0, maxval=499)\n",
    "\n",
    "sim_tanks = tfd.Normal(sample_a_bar[idxs], sample_sigma[idxs]).sample(seed=key())\n",
    "\n",
    "# transform to probability and visualize\n",
    "az.plot_kde(jax.nn.sigmoid(sim_tanks), bw=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.2 Varying effects and the underlying/overfitting trade-off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.2.1 The model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.2.2 Assign values to the parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_bar = 1.5\n",
    "sigma = 1.5\n",
    "nponds = 60\n",
    "Ni = np.repeat(np.array([5, 10, 25, 35]), repeats=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_pond = tfd.Normal(a_bar, sigma).sample((nponds,), seed=key())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pond</th>\n",
       "      <th>Ni</th>\n",
       "      <th>true_a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.909692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.687698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.038762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.870575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.377475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.850885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.411248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.448445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.256459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1.140994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pond  Ni    true_a\n",
       "0     0   5  0.909692\n",
       "1     1   5  0.687698\n",
       "2     2   5  3.038762\n",
       "3     3   5  2.870575\n",
       "4     4   5  1.377475\n",
       "5     5   5  0.850885\n",
       "6     6   5 -0.411248\n",
       "7     7   5  1.448445\n",
       "8     8   5  2.256459\n",
       "9     9   5  1.140994"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsim = pd.DataFrame(dict(pond=np.arange(nponds), Ni=Ni, true_a=a_pond))\n",
    "dsim.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.2.3 Simulate survivors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsim[\"Si\"] = tfd.Binomial(total_count=dsim.Ni.values.astype(np.float32), \n",
    "                          logits=dsim.true_a.values.astype(np.float32)).sample(seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pond</th>\n",
       "      <th>Ni</th>\n",
       "      <th>true_a</th>\n",
       "      <th>Si</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.909692</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.687698</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.038762</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.870575</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1.377475</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.850885</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.411248</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1.448445</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.256459</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1.140994</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pond  Ni    true_a   Si\n",
       "0     0   5  0.909692  2.0\n",
       "1     1   5  0.687698  4.0\n",
       "2     2   5  3.038762  5.0\n",
       "3     3   5  2.870575  5.0\n",
       "4     4   5  1.377475  4.0\n",
       "5     5   5  0.850885  4.0\n",
       "6     6   5 -0.411248  1.0\n",
       "7     7   5  1.448445  5.0\n",
       "8     8   5  2.256459  5.0\n",
       "9     9   5  1.140994  3.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsim.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.2.4 Compute the no-pooling estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsim[\"p_nopool\"] = dsim.Si / dsim.Ni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.2.5 Compute partial pooling estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_shape = dsim.shape[0]\n",
    "\n",
    "def model_13_3(Ni):\n",
    "\tdef _generator():\n",
    "\t\ta_bar = yield tfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma = yield tfd.Exponential(rate=1.0, name='sigma')\n",
    "\t\ta_pond = yield tfd.Sample(\n",
    "\t\t\t\ttfd.Normal(loc=a_bar, scale=sigma), \n",
    "\t\t\t\tsample_shape=sample_shape, name='a_pond')\n",
    "\n",
    "\t\tSi = yield tfd.Binomial(total_count=Ni, logits=a_pond, name='Si')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_3 = model_13_3(dsim.Ni.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_3_pin = jd_13_3.experimental_pin(Si=dsim.Si.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=500):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_3_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Exp(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_3_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_3 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_3_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_3._asdict().items()}\n",
    "dict_3 = dict(posterior=post_3_dict)\n",
    "idata_3 = az.from_dict(**dict_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a_bar</th>\n",
       "      <td>1.694</td>\n",
       "      <td>0.184</td>\n",
       "      <td>1.379</td>\n",
       "      <td>1.962</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1338.0</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[0]</th>\n",
       "      <td>0.473</td>\n",
       "      <td>0.769</td>\n",
       "      <td>-0.731</td>\n",
       "      <td>1.750</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[1]</th>\n",
       "      <td>1.623</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.400</td>\n",
       "      <td>3.086</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[2]</th>\n",
       "      <td>2.346</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.695</td>\n",
       "      <td>3.544</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[3]</th>\n",
       "      <td>2.315</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.814</td>\n",
       "      <td>3.695</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1304.0</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[56]</th>\n",
       "      <td>1.643</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.954</td>\n",
       "      <td>2.296</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2918.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[57]</th>\n",
       "      <td>0.905</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.329</td>\n",
       "      <td>1.427</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[58]</th>\n",
       "      <td>1.322</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.690</td>\n",
       "      <td>1.934</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2979.0</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_pond[59]</th>\n",
       "      <td>0.650</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.109</td>\n",
       "      <td>1.230</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.081</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.825</td>\n",
       "      <td>1.344</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>637.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a_bar       1.694  0.184     1.379      1.962      0.005    0.004    1338.0   \n",
       "a_pond[0]   0.473  0.769    -0.731      1.750      0.018    0.017    1914.0   \n",
       "a_pond[1]   1.623  0.852     0.400      3.086      0.022    0.017    1543.0   \n",
       "a_pond[2]   2.346  0.907     0.695      3.544      0.025    0.018    1378.0   \n",
       "a_pond[3]   2.315  0.928     0.814      3.695      0.026    0.020    1304.0   \n",
       "...           ...    ...       ...        ...        ...      ...       ...   \n",
       "a_pond[56]  1.643  0.418     0.954      2.296      0.008    0.006    2918.0   \n",
       "a_pond[57]  0.905  0.345     0.329      1.427      0.007    0.005    2553.0   \n",
       "a_pond[58]  1.322  0.400     0.690      1.934      0.007    0.006    2979.0   \n",
       "a_pond[59]  0.650  0.350     0.109      1.230      0.007    0.006    2490.0   \n",
       "sigma       1.081  0.165     0.825      1.344      0.006    0.005     637.0   \n",
       "\n",
       "            ess_tail  r_hat  \n",
       "a_bar         1621.0    1.0  \n",
       "a_pond[0]     1219.0    1.0  \n",
       "a_pond[1]     1274.0    1.0  \n",
       "a_pond[2]     1120.0    1.0  \n",
       "a_pond[3]     1058.0    1.0  \n",
       "...              ...    ...  \n",
       "a_pond[56]    1343.0    1.0  \n",
       "a_pond[57]    1395.0    1.0  \n",
       "a_pond[58]    1171.0    1.0  \n",
       "a_pond[59]    1225.0    1.0  \n",
       "sigma         1217.0    1.0  \n",
       "\n",
       "[62 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_3, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_a_pond = post_3_dict[\"a_pond\"][0]\n",
    "dsim[\"p_partpool\"] = jnp.mean(jax.nn.sigmoid(sample_a_pond), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsim[\"p_true\"] = jax.nn.sigmoid(dsim.true_a.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "nopool_error = (dsim.p_nopool - dsim.p_true).abs()\n",
    "partpool_error = (dsim.p_partpool - dsim.p_true).abs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(1, 61), nopool_error, label=\"nopool\", alpha=0.8)\n",
    "plt.gca().set(xlabel=\"pond\", ylabel=\"absolute error\")\n",
    "plt.scatter(range(1, 61), partpool_error, label=\"partpool\", alpha=0.8)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.3 More than one type of cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.3.1 Multilevel chimpanzees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv(\"./data/Chimpanzees.csv\", sep=\";\")\n",
    "\n",
    "d[\"treatment\"] = 1 + d.prosoc_left + 2 * d.condition - 1\n",
    "d[\"block_id\"] = d.block.values - 1\n",
    "d[\"actor\"] = d.actor.values - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_4(actor, block_id, treatment):\n",
    "\tdef _generator():\n",
    "\t\t# hyper-priors\n",
    "\t\ta_bar = yield tfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma_a = yield tfd.Exponential(rate=1.0, name='sigma_a')\n",
    "\t\tsigma_g = yield tfd.Exponential(rate=1.0, name='sigma_g')\n",
    "\t\t# adaptive priors\n",
    "\t\ta = yield tfd.Sample(tfd.Normal(loc=a_bar, scale=sigma_a), sample_shape=7, name='a')\n",
    "\t\tg = yield tfd.Sample(tfd.Normal(loc=0.0, scale=sigma_g), sample_shape=6, name='g')\n",
    "\t\tb = yield tfd.Sample(tfd.Normal(loc=0.0, scale=0.5), sample_shape=4, name='b')\n",
    "\n",
    "\t\tlogit = jnp.take(a, actor) + jnp.take(g, block_id) + jnp.take(b, treatment)\n",
    "\t\tyield tfd.Bernoulli(logits=logit, name='pulled_left')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_4 = model_13_4(d.actor.values.astype(np.int32), d.block_id.values.astype(np.int32),\n",
    "                     d.treatment.values.astype(np.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_4_pin = jd_13_4.experimental_pin(pulled_left=d.pulled_left.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_4_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Exp(), tfb.Exp(),\n",
    "               tfb.Identity(), tfb.Identity(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_4_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_4 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_4_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_4._asdict().items()}\n",
    "dict_4 = dict(posterior=post_4_dict)\n",
    "idata_4 = az.from_dict(**dict_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>-0.344</td>\n",
       "      <td>0.355</td>\n",
       "      <td>-0.891</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>471.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>4.562</td>\n",
       "      <td>1.070</td>\n",
       "      <td>2.736</td>\n",
       "      <td>6.009</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.039</td>\n",
       "      <td>366.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[2]</th>\n",
       "      <td>-0.635</td>\n",
       "      <td>0.395</td>\n",
       "      <td>-1.221</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.046</td>\n",
       "      <td>38.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[3]</th>\n",
       "      <td>-0.643</td>\n",
       "      <td>0.365</td>\n",
       "      <td>-1.207</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.015</td>\n",
       "      <td>294.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[4]</th>\n",
       "      <td>-0.362</td>\n",
       "      <td>0.353</td>\n",
       "      <td>-0.936</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.010</td>\n",
       "      <td>597.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[5]</th>\n",
       "      <td>0.616</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.071</td>\n",
       "      <td>1.194</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>432.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[6]</th>\n",
       "      <td>2.111</td>\n",
       "      <td>0.445</td>\n",
       "      <td>1.426</td>\n",
       "      <td>2.843</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.011</td>\n",
       "      <td>751.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_bar</th>\n",
       "      <td>0.641</td>\n",
       "      <td>0.703</td>\n",
       "      <td>-0.475</td>\n",
       "      <td>1.676</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
       "      <td>401.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[0]</th>\n",
       "      <td>-0.157</td>\n",
       "      <td>0.302</td>\n",
       "      <td>-0.621</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
       "      <td>85.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[1]</th>\n",
       "      <td>0.376</td>\n",
       "      <td>0.295</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.018</td>\n",
       "      <td>137.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[2]</th>\n",
       "      <td>-0.487</td>\n",
       "      <td>0.293</td>\n",
       "      <td>-0.938</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>510.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[3]</th>\n",
       "      <td>0.238</td>\n",
       "      <td>0.322</td>\n",
       "      <td>-0.363</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.042</td>\n",
       "      <td>34.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[0]</th>\n",
       "      <td>-0.170</td>\n",
       "      <td>0.226</td>\n",
       "      <td>-0.521</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>362.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[1]</th>\n",
       "      <td>0.037</td>\n",
       "      <td>0.187</td>\n",
       "      <td>-0.234</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[2]</th>\n",
       "      <td>0.055</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[3]</th>\n",
       "      <td>0.019</td>\n",
       "      <td>0.185</td>\n",
       "      <td>-0.231</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[4]</th>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.192</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1612.0</td>\n",
       "      <td>1392.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[5]</th>\n",
       "      <td>0.118</td>\n",
       "      <td>0.226</td>\n",
       "      <td>-0.219</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1122.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_a</th>\n",
       "      <td>1.979</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1.079</td>\n",
       "      <td>2.762</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.017</td>\n",
       "      <td>454.0</td>\n",
       "      <td>945.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_g</th>\n",
       "      <td>0.227</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a[0]    -0.344  0.355    -0.891      0.231      0.016    0.012     471.0   \n",
       "a[1]     4.562  1.070     2.736      6.009      0.055    0.039     366.0   \n",
       "a[2]    -0.635  0.395    -1.221      0.035      0.064    0.046      38.0   \n",
       "a[3]    -0.643  0.365    -1.207     -0.062      0.021    0.015     294.0   \n",
       "a[4]    -0.362  0.353    -0.936      0.167      0.014    0.010     597.0   \n",
       "a[5]     0.616  0.361     0.071      1.194      0.017    0.012     432.0   \n",
       "a[6]     2.111  0.445     1.426      2.843      0.016    0.011     751.0   \n",
       "a_bar    0.641  0.703    -0.475      1.676      0.034    0.024     401.0   \n",
       "b[0]    -0.157  0.302    -0.621      0.302      0.034    0.024      85.0   \n",
       "b[1]     0.376  0.295    -0.061      0.826      0.026    0.018     137.0   \n",
       "b[2]    -0.487  0.293    -0.938     -0.039      0.013    0.009     510.0   \n",
       "b[3]     0.238  0.322    -0.363      0.655      0.059    0.042      34.0   \n",
       "g[0]    -0.170  0.226    -0.521      0.119      0.011    0.008     362.0   \n",
       "g[1]     0.037  0.187    -0.234      0.325      0.005    0.004    1542.0   \n",
       "g[2]     0.055  0.191    -0.229      0.350      0.005    0.004    1585.0   \n",
       "g[3]     0.019  0.185    -0.231      0.316      0.005    0.004    1765.0   \n",
       "g[4]    -0.023  0.192    -0.271      0.283      0.005    0.004    1612.0   \n",
       "g[5]     0.118  0.226    -0.219      0.423      0.007    0.005    1122.0   \n",
       "sigma_a  1.979  0.588     1.079      2.762      0.024    0.017     454.0   \n",
       "sigma_g  0.227  0.201     0.049      0.428      0.013    0.009      28.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "a[0]        924.0   1.02  \n",
       "a[1]        484.0   1.05  \n",
       "a[2]        126.0   1.08  \n",
       "a[3]       1064.0   1.03  \n",
       "a[4]        980.0   1.01  \n",
       "a[5]       1001.0   1.02  \n",
       "a[6]        639.0   1.02  \n",
       "a_bar       702.0   1.02  \n",
       "b[0]        666.0   1.04  \n",
       "b[1]        971.0   1.03  \n",
       "b[2]       1104.0   1.01  \n",
       "b[3]         17.0   1.09  \n",
       "g[0]        924.0   1.01  \n",
       "g[1]       1215.0   1.08  \n",
       "g[2]       1172.0   1.04  \n",
       "g[3]       1077.0   1.05  \n",
       "g[4]       1392.0   1.01  \n",
       "g[5]        909.0   1.02  \n",
       "sigma_a     945.0   1.01  \n",
       "sigma_g      14.0   1.11  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_4, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there is variation across parameters when it comes to effective sample size (ess_mean). This is because some parameters spends a lot of time near a boundary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(idata_4, combined=True, hdi_prob=0.89);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.23\n",
    "\n",
    "Build a model that ignores block so that we can then compare it with the above model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_5(actor, treatment):\n",
    "\tdef _generator():\n",
    "\t\t# hyper-priors\n",
    "\t\ta_bar = yield tfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma_a = yield tfd.Exponential(rate=1.0, name='sigma_a')\n",
    "\t\t# adaptive priors\n",
    "\t\ta = yield tfd.Sample(tfd.Normal(loc=a_bar, scale=sigma_a), sample_shape=7, name='a')\n",
    "\t\tb = yield tfd.Sample(tfd.Normal(loc=0.0, scale=0.5), sample_shape=4, name='b')\n",
    "\n",
    "\t\tlogit = jnp.take(a, actor) + jnp.take(b, treatment)\n",
    "\t\tyield tfd.Bernoulli(logits=logit, name='pulled_left')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_5 = model_13_5(d.actor.values.astype(np.int32), d.treatment.values.astype(np.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_5_pin = jd_13_5.experimental_pin(pulled_left=d.pulled_left.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_5_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Exp(), tfb.Identity(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_5_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_5 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_5_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_5._asdict().items()}\n",
    "dict_5 = dict(posterior=post_5_dict)\n",
    "idata_5 = az.from_dict(**dict_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>-0.359</td>\n",
       "      <td>0.354</td>\n",
       "      <td>-0.909</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>4.615</td>\n",
       "      <td>1.287</td>\n",
       "      <td>2.786</td>\n",
       "      <td>6.197</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.041</td>\n",
       "      <td>665.0</td>\n",
       "      <td>488.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[2]</th>\n",
       "      <td>-0.647</td>\n",
       "      <td>0.358</td>\n",
       "      <td>-1.182</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>1515.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[3]</th>\n",
       "      <td>-0.651</td>\n",
       "      <td>0.351</td>\n",
       "      <td>-1.207</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[4]</th>\n",
       "      <td>-0.355</td>\n",
       "      <td>0.348</td>\n",
       "      <td>-0.878</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[5]</th>\n",
       "      <td>0.584</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.032</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1646.0</td>\n",
       "      <td>1431.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[6]</th>\n",
       "      <td>2.103</td>\n",
       "      <td>0.462</td>\n",
       "      <td>1.326</td>\n",
       "      <td>2.818</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_bar</th>\n",
       "      <td>0.588</td>\n",
       "      <td>0.696</td>\n",
       "      <td>-0.526</td>\n",
       "      <td>1.647</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[0]</th>\n",
       "      <td>-0.135</td>\n",
       "      <td>0.304</td>\n",
       "      <td>-0.585</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[1]</th>\n",
       "      <td>0.392</td>\n",
       "      <td>0.302</td>\n",
       "      <td>-0.083</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>1391.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[2]</th>\n",
       "      <td>-0.473</td>\n",
       "      <td>0.300</td>\n",
       "      <td>-0.916</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[3]</th>\n",
       "      <td>0.283</td>\n",
       "      <td>0.299</td>\n",
       "      <td>-0.175</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_a</th>\n",
       "      <td>1.989</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.061</td>\n",
       "      <td>2.899</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.016</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a[0]    -0.359  0.354    -0.909      0.213      0.010    0.008    1382.0   \n",
       "a[1]     4.615  1.287     2.786      6.197      0.056    0.041     665.0   \n",
       "a[2]    -0.647  0.358    -1.182     -0.045      0.009    0.007    1515.0   \n",
       "a[3]    -0.651  0.351    -1.207     -0.093      0.009    0.006    1541.0   \n",
       "a[4]    -0.355  0.348    -0.878      0.208      0.009    0.007    1603.0   \n",
       "a[5]     0.584  0.353     0.032      1.160      0.009    0.006    1646.0   \n",
       "a[6]     2.103  0.462     1.326      2.818      0.011    0.008    1741.0   \n",
       "a_bar    0.588  0.696    -0.526      1.647      0.017    0.014    1627.0   \n",
       "b[0]    -0.135  0.304    -0.585      0.375      0.008    0.006    1578.0   \n",
       "b[1]     0.392  0.302    -0.083      0.892      0.008    0.006    1537.0   \n",
       "b[2]    -0.473  0.300    -0.916      0.026      0.008    0.006    1400.0   \n",
       "b[3]     0.283  0.299    -0.175      0.771      0.008    0.006    1551.0   \n",
       "sigma_a  1.989  0.667     1.061      2.899      0.022    0.016    1148.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "a[0]       1226.0   1.00  \n",
       "a[1]        488.0   1.00  \n",
       "a[2]       1520.0   1.00  \n",
       "a[3]       1667.0   1.00  \n",
       "a[4]       1484.0   1.00  \n",
       "a[5]       1431.0   1.00  \n",
       "a[6]       1428.0   1.00  \n",
       "a_bar      1347.0   1.01  \n",
       "b[0]       1211.0   1.00  \n",
       "b[1]       1391.0   1.00  \n",
       "b[2]       1290.0   1.00  \n",
       "b[3]       1320.0   1.00  \n",
       "sigma_a    1203.0   1.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_5, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_4x(actor, block_id, treatment):\n",
    "\tdef _generator():\n",
    "\t\t# hyper-priors\n",
    "\t\ta_bar = yield xtfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma_a = yield xtfd.Exponential(rate=1.0, name='sigma_a')\n",
    "\t\tsigma_g = yield xtfd.Exponential(rate=1.0, name='sigma_g')\n",
    "\t\t# adaptive priors\n",
    "\t\ta = yield xtfd.Sample(xtfd.Normal(loc=a_bar, scale=sigma_a), sample_shape=7, name='a')\n",
    "\t\tg = yield xtfd.Sample(xtfd.Normal(loc=0.0, scale=sigma_g), sample_shape=6, name='g')\n",
    "\t\tb = yield xtfd.Sample(xtfd.Normal(loc=0.0, scale=0.5), sample_shape=4, name='b')\n",
    "\n",
    "\t\tlogit = tf.gather(a, actor) + tf.gather(g, block_id) + tf.gather(b, treatment)\n",
    "\t\tyield xtfd.Bernoulli(logits=logit, name='pulled_left')\n",
    "\n",
    "\treturn xtfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n"
     ]
    }
   ],
   "source": [
    "jd_4x = model_13_4x(d.actor.values.astype(np.int32), d.block_id.values.astype(np.int32),\n",
    "                     d.treatment.values.astype(np.int32))\n",
    "ds_4, _ = jd_4x.sample_distributions(value=post_4)\n",
    "ll_4 = ds_4[-1].log_prob(d.pulled_left.values.astype(np.float32))\n",
    "ll_4 = np.moveaxis(ll_4, 0, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_4.update(log_likelihood={'ll': ll_4},\n",
    "              dims={\"ll\": [\"obs\"]},\n",
    "              coords={\"obs\": np.arange(len(d.pulled_left.values))})\n",
    "idata_4 = az.from_dict(**dict_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_5x(actor, treatment):\n",
    "\tdef _generator():\n",
    "\t\t# hyper-priors\n",
    "\t\ta_bar = yield xtfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma_a = yield xtfd.Exponential(rate=1.0, name='sigma_a')\n",
    "\t\t# adaptive priors\n",
    "\t\ta = yield xtfd.Sample(xtfd.Normal(loc=a_bar, scale=sigma_a), sample_shape=7, name='a')\n",
    "\t\tb = yield xtfd.Sample(xtfd.Normal(loc=0.0, scale=0.5), sample_shape=4, name='b')\n",
    "\n",
    "\t\tlogit = tf.gather(a, actor) + tf.gather(b, treatment)\n",
    "\t\tyield xtfd.Bernoulli(logits=logit, name='pulled_left')\n",
    "\n",
    "\treturn xtfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter cause there is no registered converter for this op.\n",
      "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n"
     ]
    }
   ],
   "source": [
    "jd_5x = model_13_5x(d.actor.values.astype(np.int32), d.treatment.values.astype(np.int32))\n",
    "ds_5, _ = jd_5x.sample_distributions(value=post_5)\n",
    "ll_5 = ds_5[-1].log_prob(d.pulled_left.values.astype(np.float32))\n",
    "ll_5 = np.moveaxis(ll_5, 0, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_5.update(log_likelihood={'ll': ll_5},\n",
    "              dims={\"ll\": [\"obs\"]},\n",
    "              coords={\"obs\": np.arange(len(d.pulled_left.values))})\n",
    "idata_5 = az.from_dict(**dict_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>rank</th>\n",
       "      <th>elpd_waic</th>\n",
       "      <th>p_waic</th>\n",
       "      <th>elpd_diff</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>m13.5</th>\n",
       "      <td>0</td>\n",
       "      <td>530.976480</td>\n",
       "      <td>8.435287</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.173765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>deviance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m13.4</th>\n",
       "      <td>1</td>\n",
       "      <td>532.503967</td>\n",
       "      <td>10.656053</td>\n",
       "      <td>1.527487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.311229</td>\n",
       "      <td>1.726081</td>\n",
       "      <td>False</td>\n",
       "      <td>deviance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank   elpd_waic     p_waic  elpd_diff  weight         se       dse  \\\n",
       "m13.5     0  530.976480   8.435287   0.000000     1.0  19.173765  0.000000   \n",
       "m13.4     1  532.503967  10.656053   1.527487     0.0  19.311229  1.726081   \n",
       "\n",
       "       warning     scale  \n",
       "m13.5    False  deviance  \n",
       "m13.4    False  deviance  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.compare({\"m13.4\": idata_4, \"m13.5\": idata_5}, ic='waic', scale=\"deviance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.3.2 Even more clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.25\n",
    "\n",
    "m13.4 with partial pooling on the treatments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_6(actor, block_id, treatment):\n",
    "\tdef _generator():\n",
    "\t\t# hyper-priors\n",
    "\t\ta_bar = yield tfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma_a = yield tfd.Exponential(rate=1.0, name='sigma_a')\n",
    "\t\tsigma_g = yield tfd.Exponential(rate=1.0, name='sigma_g')\n",
    "\t\tsigma_b = yield tfd.Exponential(rate=1.0, name='sigma_b')\n",
    "\t\t# adaptive priors\n",
    "\t\ta = yield tfd.Sample(tfd.Normal(loc=a_bar, scale=sigma_a), sample_shape=7, name='a')\n",
    "\t\tg = yield tfd.Sample(tfd.Normal(loc=0.0, scale=sigma_g), sample_shape=6, name='g')\n",
    "\t\tb = yield tfd.Sample(tfd.Normal(loc=0.0, scale=sigma_b), sample_shape=4, name='b')\n",
    "\n",
    "\t\tlogit = jnp.take(a, actor) + jnp.take(g, block_id) + jnp.take(b, treatment)\n",
    "\t\tyield tfd.Bernoulli(logits=logit, name='pulled_left')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_6 = model_13_6(d.actor.values.astype(np.int32), d.block_id.values.astype(np.int32),\n",
    "                     d.treatment.values.astype(np.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_6_pin = jd_13_6.experimental_pin(pulled_left=d.pulled_left.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_6_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Exp(), tfb.Exp(), tfb.Exp(),\n",
    "               tfb.Identity(), tfb.Identity(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_6_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_6 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_6_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_6._asdict().items()}\n",
    "dict_6 = dict(posterior=post_6_dict)\n",
    "idata_6 = az.from_dict(**dict_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a[0]</th>\n",
       "      <td>-0.381</td>\n",
       "      <td>0.413</td>\n",
       "      <td>-1.049</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.012</td>\n",
       "      <td>788.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[1]</th>\n",
       "      <td>4.562</td>\n",
       "      <td>1.214</td>\n",
       "      <td>2.685</td>\n",
       "      <td>6.320</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.045</td>\n",
       "      <td>387.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[2]</th>\n",
       "      <td>-0.670</td>\n",
       "      <td>0.426</td>\n",
       "      <td>-1.327</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.012</td>\n",
       "      <td>724.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[3]</th>\n",
       "      <td>-0.674</td>\n",
       "      <td>0.407</td>\n",
       "      <td>-1.287</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.011</td>\n",
       "      <td>804.0</td>\n",
       "      <td>778.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[4]</th>\n",
       "      <td>-0.371</td>\n",
       "      <td>0.408</td>\n",
       "      <td>-1.002</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.012</td>\n",
       "      <td>767.0</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[5]</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.412</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>1.245</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.011</td>\n",
       "      <td>766.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a[6]</th>\n",
       "      <td>2.102</td>\n",
       "      <td>0.520</td>\n",
       "      <td>1.274</td>\n",
       "      <td>2.948</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.015</td>\n",
       "      <td>637.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_bar</th>\n",
       "      <td>0.554</td>\n",
       "      <td>0.735</td>\n",
       "      <td>-0.549</td>\n",
       "      <td>1.776</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.022</td>\n",
       "      <td>570.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[0]</th>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.363</td>\n",
       "      <td>-0.686</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>648.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[1]</th>\n",
       "      <td>0.387</td>\n",
       "      <td>0.374</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.012</td>\n",
       "      <td>607.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[2]</th>\n",
       "      <td>-0.442</td>\n",
       "      <td>0.364</td>\n",
       "      <td>-1.013</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.010</td>\n",
       "      <td>757.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[3]</th>\n",
       "      <td>0.288</td>\n",
       "      <td>0.362</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "      <td>748.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[0]</th>\n",
       "      <td>-0.201</td>\n",
       "      <td>0.235</td>\n",
       "      <td>-0.555</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>709.0</td>\n",
       "      <td>859.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[1]</th>\n",
       "      <td>0.043</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[2]</th>\n",
       "      <td>0.057</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-0.233</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1590.0</td>\n",
       "      <td>1386.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[3]</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.194</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>1153.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[4]</th>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.191</td>\n",
       "      <td>-0.315</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2089.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g[5]</th>\n",
       "      <td>0.125</td>\n",
       "      <td>0.203</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_a</th>\n",
       "      <td>1.986</td>\n",
       "      <td>0.597</td>\n",
       "      <td>1.162</td>\n",
       "      <td>2.926</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.015</td>\n",
       "      <td>811.0</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_b</th>\n",
       "      <td>0.585</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.010</td>\n",
       "      <td>597.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_g</th>\n",
       "      <td>0.243</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>251.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a[0]    -0.381  0.413    -1.049      0.234      0.015    0.012     788.0   \n",
       "a[1]     4.562  1.214     2.685      6.320      0.063    0.045     387.0   \n",
       "a[2]    -0.670  0.426    -1.327     -0.009      0.016    0.012     724.0   \n",
       "a[3]    -0.674  0.407    -1.287     -0.028      0.015    0.011     804.0   \n",
       "a[4]    -0.371  0.408    -1.002      0.265      0.015    0.012     767.0   \n",
       "a[5]     0.566  0.412    -0.058      1.245      0.015    0.011     766.0   \n",
       "a[6]     2.102  0.520     1.274      2.948      0.021    0.015     637.0   \n",
       "a_bar    0.554  0.735    -0.549      1.776      0.031    0.022     570.0   \n",
       "b[0]    -0.109  0.363    -0.686      0.398      0.014    0.011     648.0   \n",
       "b[1]     0.387  0.374    -0.203      0.941      0.015    0.012     607.0   \n",
       "b[2]    -0.442  0.364    -1.013      0.090      0.013    0.010     757.0   \n",
       "b[3]     0.288  0.362    -0.227      0.876      0.014    0.011     748.0   \n",
       "g[0]    -0.201  0.235    -0.555      0.131      0.009    0.007     709.0   \n",
       "g[1]     0.043  0.191    -0.229      0.357      0.005    0.005    1798.0   \n",
       "g[2]     0.057  0.197    -0.233      0.372      0.005    0.005    1590.0   \n",
       "g[3]     0.011  0.194    -0.273      0.327      0.005    0.005    1938.0   \n",
       "g[4]    -0.031  0.191    -0.315      0.275      0.004    0.005    2089.0   \n",
       "g[5]     0.125  0.203    -0.167      0.438      0.006    0.005    1061.0   \n",
       "sigma_a  1.986  0.597     1.162      2.926      0.021    0.015     811.0   \n",
       "sigma_b  0.585  0.349     0.137      0.987      0.014    0.010     597.0   \n",
       "sigma_g  0.243  0.163     0.061      0.436      0.008    0.006     251.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "a[0]        616.0   1.01  \n",
       "a[1]        509.0   1.01  \n",
       "a[2]        628.0   1.00  \n",
       "a[3]        778.0   1.01  \n",
       "a[4]        796.0   1.00  \n",
       "a[5]        662.0   1.00  \n",
       "a[6]        326.0   1.01  \n",
       "a_bar       406.0   1.00  \n",
       "b[0]        540.0   1.01  \n",
       "b[1]        243.0   1.01  \n",
       "b[2]        677.0   1.00  \n",
       "b[3]        622.0   1.00  \n",
       "g[0]        859.0   1.01  \n",
       "g[1]       1303.0   1.00  \n",
       "g[2]       1386.0   1.00  \n",
       "g[3]       1153.0   1.00  \n",
       "g[4]       1218.0   1.00  \n",
       "g[5]       1320.0   1.00  \n",
       "sigma_a    1407.0   1.01  \n",
       "sigma_b     520.0   1.00  \n",
       "sigma_g     120.0   1.01  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_6, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.4 Divergent transitions and non-centered priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.4.1 The Devil's Funnel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.26 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_7():\n",
    "    def _generator():\n",
    "        v = yield tfd.Normal(loc=0.0, scale=3.0, name='v')\n",
    "        yield tfd.Normal(loc=0.0, scale=jnp.exp(v), name='x')\n",
    "\n",
    "    return tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_7 = model_13_7()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_7.log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_7.sample(num_chains, seed=key())\n",
    "post_7 = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_7_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_7._asdict().items()}\n",
    "dict_7 = dict(posterior=post_7_dict)\n",
    "idata_7 = az.from_dict(**dict_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>1.067</td>\n",
       "      <td>2.254</td>\n",
       "      <td>-2.554</td>\n",
       "      <td>4.046</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.374</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>-6.800</td>\n",
       "      <td>52.608</td>\n",
       "      <td>-49.142</td>\n",
       "      <td>20.161</td>\n",
       "      <td>12.083</td>\n",
       "      <td>8.679</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean      sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "v  1.067   2.254    -2.554      4.046      0.520    0.374      20.0      16.0   \n",
       "x -6.800  52.608   -49.142     20.161     12.083    8.679      18.0      27.0   \n",
       "\n",
       "   r_hat  \n",
       "v   1.18  \n",
       "x   1.17  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_7, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.27"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Expected `tfd.Deterministic` works like Stan's `transformed parameters` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_7nc():\n",
    "    def _generator():\n",
    "        v = yield tfd.Normal(loc=0.0, scale=3.0, name='v')\n",
    "        z = yield tfd.Normal(loc=0.0, scale=1.0, name='z')\n",
    "        # yield tfd.Deterministic(z * jnp.exp(v), name='x')\n",
    "\n",
    "    return tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_7nc = model_13_7nc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_7nc.log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Identity()]#, tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_7nc.sample(num_chains, seed=key())\n",
    "post_7nc = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_7nc_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_7nc._asdict().items()}\n",
    "dict_7nc = dict(posterior=post_7nc_dict)\n",
    "idata_7nc = az.from_dict(**dict_7nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>0.098</td>\n",
       "      <td>3.003</td>\n",
       "      <td>-4.507</td>\n",
       "      <td>4.765</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.077</td>\n",
       "      <td>800.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>-0.015</td>\n",
       "      <td>1.026</td>\n",
       "      <td>-1.714</td>\n",
       "      <td>1.585</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1864.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "v  0.098  3.003    -4.507      4.765      0.108    0.077     800.0     847.0   \n",
       "z -0.015  1.026    -1.714      1.585      0.024    0.020    1864.0    1695.0   \n",
       "\n",
       "   r_hat  \n",
       "v   1.01  \n",
       "z   1.00  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_7nc, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.4.2 Non-centered chimpanzees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_13_4nc(actor, block_id, treatment):\n",
    "\tdef _generator():\n",
    "\t\t# hyper-priors\n",
    "\t\ta_bar = yield tfd.Normal(loc=0.0, scale=1.5, name='a_bar')\n",
    "\t\tsigma_a = yield tfd.Exponential(rate=1.0, name='sigma_a')\n",
    "\t\tsigma_g = yield tfd.Exponential(rate=1.0, name='sigma_g')\n",
    "\t\t# adaptive priors\n",
    "\t\tz = yield tfd.Sample(tfd.Normal(loc=0.0, scale=1.0), sample_shape=7, name='z')\n",
    "\t\tx = yield tfd.Sample(tfd.Normal(loc=0.0, scale=1.0), sample_shape=6, name='x')\n",
    "\t\tb = yield tfd.Sample(tfd.Normal(loc=0.0, scale=0.5), sample_shape=4, name='b')\n",
    "\n",
    "\t\t# reparamertization\n",
    "\t\tlogit = a_bar + sigma_a * jnp.take(z, actor) + \\\n",
    "\t\t\tsigma_g * jnp.take(x, block_id) + jnp.take(b, treatment)\n",
    "\n",
    "\t\tyield tfd.Bernoulli(logits=logit, name='pulled_left')\n",
    "\n",
    "\treturn tfd.JointDistributionCoroutineAutoBatched(_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_4nc = model_13_4nc(d.actor.values.astype(np.int32), \n",
    "                         d.block_id.values.astype(np.int32),\n",
    "                         d.treatment.values.astype(np.int32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "jd_13_4nc_pin = jd_13_4nc.experimental_pin(pulled_left=d.pulled_left.values.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jax.jit\n",
    "def run_chain(init_state, seed, step_size=1e-3, num_steps=500, burnin=400):\n",
    "\n",
    "  def target_log_prob(*states):\n",
    "    return jd_13_4nc_pin.unnormalized_log_prob(states)\n",
    "  \n",
    "  bijectors = [tfb.Identity(), tfb.Exp(), tfb.Exp(),\n",
    "               tfb.Identity(), tfb.Identity(), tfb.Identity()]\n",
    "\n",
    "  kernel = tfp.mcmc.NoUTurnSampler(\n",
    "      target_log_prob_fn=target_log_prob,\n",
    "      step_size=step_size)\n",
    "  \n",
    "  kernel = tfp.mcmc.TransformedTransitionKernel(\n",
    "    inner_kernel=kernel,\n",
    "    bijector=bijectors)\n",
    "\n",
    "  kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(\n",
    "    inner_kernel=kernel,\n",
    "    num_adaptation_steps=burnin)\n",
    "\n",
    "  chain_state = tfp.mcmc.sample_chain(\n",
    "      num_results=num_steps,\n",
    "      num_burnin_steps=burnin,\n",
    "      current_state=init_state,\n",
    "      kernel=kernel,\n",
    "      seed=seed,\n",
    "      trace_fn=None)\n",
    "  \n",
    "  return chain_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_chains = 4\n",
    "init_state = jd_13_4nc_pin.sample_unpinned(num_chains, seed=key())\n",
    "post_4nc = run_chain(init_state=init_state, seed=key())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_4nc_dict = {k: jnp.moveaxis(v, 0, 1) for k, v in post_4nc._asdict().items()}\n",
    "dict_4nc = dict(posterior=post_4nc_dict)\n",
    "idata_4nc = az.from_dict(**dict_4nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_5.5%</th>\n",
       "      <th>hdi_94.5%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a_bar</th>\n",
       "      <td>0.537</td>\n",
       "      <td>0.694</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>1.651</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.018</td>\n",
       "      <td>763.0</td>\n",
       "      <td>782.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[0]</th>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.284</td>\n",
       "      <td>-0.612</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[1]</th>\n",
       "      <td>0.394</td>\n",
       "      <td>0.289</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2252.0</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[2]</th>\n",
       "      <td>-0.475</td>\n",
       "      <td>0.290</td>\n",
       "      <td>-0.979</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1742.0</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b[3]</th>\n",
       "      <td>0.283</td>\n",
       "      <td>0.288</td>\n",
       "      <td>-0.167</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_a</th>\n",
       "      <td>1.958</td>\n",
       "      <td>0.623</td>\n",
       "      <td>1.049</td>\n",
       "      <td>2.877</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.014</td>\n",
       "      <td>876.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_g</th>\n",
       "      <td>0.204</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>556.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[0]</th>\n",
       "      <td>-0.691</td>\n",
       "      <td>0.901</td>\n",
       "      <td>-2.171</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>1176.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[1]</th>\n",
       "      <td>0.146</td>\n",
       "      <td>0.816</td>\n",
       "      <td>-1.164</td>\n",
       "      <td>1.460</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>1144.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[2]</th>\n",
       "      <td>0.244</td>\n",
       "      <td>0.826</td>\n",
       "      <td>-1.139</td>\n",
       "      <td>1.453</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[3]</th>\n",
       "      <td>0.060</td>\n",
       "      <td>0.838</td>\n",
       "      <td>-1.221</td>\n",
       "      <td>1.382</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[4]</th>\n",
       "      <td>-0.147</td>\n",
       "      <td>0.859</td>\n",
       "      <td>-1.581</td>\n",
       "      <td>1.198</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1211.0</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[5]</th>\n",
       "      <td>0.499</td>\n",
       "      <td>0.828</td>\n",
       "      <td>-0.788</td>\n",
       "      <td>1.827</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1397.0</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[0]</th>\n",
       "      <td>-0.510</td>\n",
       "      <td>0.416</td>\n",
       "      <td>-1.158</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.011</td>\n",
       "      <td>773.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[1]</th>\n",
       "      <td>2.163</td>\n",
       "      <td>0.627</td>\n",
       "      <td>1.099</td>\n",
       "      <td>3.046</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1408.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[2]</th>\n",
       "      <td>-0.678</td>\n",
       "      <td>0.431</td>\n",
       "      <td>-1.333</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.013</td>\n",
       "      <td>606.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[3]</th>\n",
       "      <td>-0.684</td>\n",
       "      <td>0.434</td>\n",
       "      <td>-1.386</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>703.0</td>\n",
       "      <td>984.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[4]</th>\n",
       "      <td>-0.512</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-1.159</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.011</td>\n",
       "      <td>775.0</td>\n",
       "      <td>788.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[5]</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.374</td>\n",
       "      <td>-0.606</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>877.0</td>\n",
       "      <td>985.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z[6]</th>\n",
       "      <td>0.856</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_5.5%  hdi_94.5%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "a_bar    0.537  0.694    -0.528      1.651      0.025    0.018     763.0   \n",
       "b[0]    -0.130  0.284    -0.612      0.295      0.006    0.006    2311.0   \n",
       "b[1]     0.394  0.289    -0.059      0.856      0.006    0.005    2252.0   \n",
       "b[2]    -0.475  0.290    -0.979     -0.053      0.007    0.006    1742.0   \n",
       "b[3]     0.283  0.288    -0.167      0.736      0.006    0.005    2021.0   \n",
       "sigma_a  1.958  0.623     1.049      2.877      0.020    0.014     876.0   \n",
       "sigma_g  0.204  0.167     0.000      0.399      0.007    0.005     556.0   \n",
       "x[0]    -0.691  0.901    -2.171      0.657      0.028    0.020    1029.0   \n",
       "x[1]     0.146  0.816    -1.164      1.460      0.022    0.020    1362.0   \n",
       "x[2]     0.244  0.826    -1.139      1.453      0.024    0.020    1201.0   \n",
       "x[3]     0.060  0.838    -1.221      1.382      0.022    0.021    1490.0   \n",
       "x[4]    -0.147  0.859    -1.581      1.198      0.025    0.021    1211.0   \n",
       "x[5]     0.499  0.828    -0.788      1.827      0.022    0.017    1397.0   \n",
       "z[0]    -0.510  0.416    -1.158      0.149      0.015    0.011     773.0   \n",
       "z[1]     2.163  0.627     1.099      3.046      0.017    0.012    1408.0   \n",
       "z[2]    -0.678  0.431    -1.333      0.035      0.018    0.013     606.0   \n",
       "z[3]    -0.684  0.434    -1.386      0.009      0.017    0.012     703.0   \n",
       "z[4]    -0.512  0.414    -1.159      0.158      0.015    0.011     775.0   \n",
       "z[5]     0.008  0.374    -0.606      0.593      0.013    0.009     877.0   \n",
       "z[6]     0.856  0.439     0.205      1.573      0.013    0.009    1082.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "a_bar       782.0   1.00  \n",
       "b[0]       1385.0   1.00  \n",
       "b[1]       1301.0   1.00  \n",
       "b[2]       1403.0   1.00  \n",
       "b[3]       1447.0   1.00  \n",
       "sigma_a    1129.0   1.00  \n",
       "sigma_g     437.0   1.00  \n",
       "x[0]       1176.0   1.00  \n",
       "x[1]       1144.0   1.00  \n",
       "x[2]       1185.0   1.01  \n",
       "x[3]       1254.0   1.00  \n",
       "x[4]       1132.0   1.00  \n",
       "x[5]       1463.0   1.00  \n",
       "z[0]        673.0   1.00  \n",
       "z[1]       1489.0   1.00  \n",
       "z[2]        944.0   1.01  \n",
       "z[3]        984.0   1.00  \n",
       "z[4]        788.0   1.00  \n",
       "z[5]        985.0   1.00  \n",
       "z[6]       1063.0   1.00  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(idata_4nc, hdi_prob=0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.5 Multilevel posterior predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.5.1 Posterior prediction for same clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a_bar', 'sigma_a', 'sigma_g', 'a', 'g', 'b'])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_4_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array(0.9940605, dtype=float32), Array([0.9925009, 0.9958693], dtype=float32))"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chimp = 2\n",
    "d_pred = dict(\n",
    "    actor=np.repeat(chimp, 4) - 1, treatment=np.arange(4), block_id=np.repeat(1, 4) - 1\n",
    ")\n",
    "\n",
    "# we want to calculate the p using the posterior\n",
    "\n",
    "sample_a = post_4_dict[\"a\"][0]\n",
    "sample_g = post_4_dict[\"g\"][0]\n",
    "sample_b = post_4_dict[\"b\"][0]\n",
    "\n",
    "term1 = jnp.take(sample_a, d_pred[\"actor\"])\n",
    "term2 = jnp.take(sample_g, d_pred[\"block_id\"])\n",
    "term3 = jnp.take(sample_b, d_pred[\"treatment\"])\n",
    "\n",
    "p = jax.nn.sigmoid(term1 + term2 + term3)\n",
    "\n",
    "p_mu = jnp.mean(p)\n",
    "p_ci = jnp.percentile(p, q=jnp.array([5.5, 94.5]), axis=0)\n",
    "\n",
    "p_mu, p_ci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_link(treatment, actor=0, block_id=0):\n",
    "    a, g, b = post_4_dict[\"a\"][0], post_4_dict[\"g\"][0], post_4_dict[\"b\"][0]\n",
    "    logodds = a[:, actor] + g[:, block_id] + b[:, treatment]\n",
    "    return tf.sigmoid(logodds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_raw = list(map(lambda i: p_link(i, actor=1, block_id=0), np.arange(4)))\n",
    "p_mu = np.mean(p_raw, 0)\n",
    "p_ci = np.percentile(p_raw, (5.5, 94.5), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.5.2 Posterior prediction for new clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_link_abar(treatment):\n",
    "    logodds = post_4_dict[\"a_bar\"][0] + post_4_dict[\"b\"][0][:, treatment]\n",
    "    return jax.nn.sigmoid(logodds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.37"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_raw = list(map(p_link_abar, np.arange(4)))\n",
    "p_mu = np.mean(p_raw, 1)\n",
    "p_ci = np.percentile(p_raw, (5.5, 94.5), 1)\n",
    "\n",
    "plt.subplot(\n",
    "    xlabel=\"treatment\", ylabel=\"proportion pulled left\", ylim=(0, 1), xlim=(0.9, 4.1)\n",
    ")\n",
    "plt.gca().set(xticks=range(1, 5), xticklabels=[\"R/N\", \"L/N\", \"R/P\", \"L/P\"])\n",
    "plt.plot(range(1, 5), p_mu)\n",
    "plt.fill_between(range(1, 5), p_ci[0], p_ci[1], color=\"k\", alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_sim = tfd.Normal(\n",
    "    loc=post_4_dict[\"a_bar\"][0], scale=post_4_dict[\"sigma_a\"][0]).sample(seed=key())\n",
    "\n",
    "def p_link_asim(treatment):\n",
    "    logodds = a_sim + post_4_dict[\"b\"][0][:, treatment]\n",
    "    return jax.nn.sigmoid(logodds)\n",
    "\n",
    "p_raw_asim = np.array(list(map(p_link_asim, np.arange(4))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code 13.39"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(\n",
    "    xlabel=\"treatment\", ylabel=\"proportion pulled left\", ylim=(0, 1), xlim=(0.9, 4.1)\n",
    ")\n",
    "plt.gca().set(xticks=range(1, 5), xticklabels=[\"R/N\", \"L/N\", \"R/P\", \"L/P\"])\n",
    "for i in range(100):\n",
    "    plt.plot(range(1, 5), p_raw_asim[:, i], color=\"k\", alpha=0.25)"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.15"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}