{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependent density regression\n",
    "In another [example](dp_mix.ipynb), we showed how to use Dirichlet processes to perform Bayesian nonparametric density estimation.  This example expands on the previous one, illustrating dependent density regression.\n",
    "\n",
    "Just as Dirichlet process mixtures can be thought of as infinite mixture models that select the number of active components as part of inference, dependent density regression can be thought of as infinite [mixtures of experts](https://en.wikipedia.org/wiki/Committee_machine) that select the active experts as part of inference.  Their flexibility and modularity make them powerful tools for performing nonparametric Bayesian Data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.11.2\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import HTML\n",
    "from matplotlib import animation as ani\n",
    "from matplotlib import pyplot as plt\n",
    "from theano import tensor as tt\n",
    "\n",
    "print(f\"Running on PyMC3 v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.rc(\"animation\", writer=\"ffmpeg\")\n",
    "blue, *_ = sns.color_palette()\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "SEED = 972915  # from random.org; for reproducibility\n",
    "np.random.seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the LIDAR data set from Larry Wasserman's excellent book, [_All of Nonparametric Statistics_](http://www.stat.cmu.edu/~larry/all-of-nonpar/).  We standardize the data set to improve the rate of convergence of our samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_URI = \"http://www.stat.cmu.edu/~larry/all-of-nonpar/=data/lidar.dat\"\n",
    "\n",
    "\n",
    "def standardize(x):\n",
    "    return (x - x.mean()) / x.std()\n",
    "\n",
    "\n",
    "df = pd.read_csv(DATA_URI, sep=r\"\\s{1,3}\", engine=\"python\").assign(\n",
    "    std_range=lambda df: standardize(df.range), std_logratio=lambda df: standardize(df.logratio)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>range</th>\n",
       "      <th>logratio</th>\n",
       "      <th>std_range</th>\n",
       "      <th>std_logratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>390</td>\n",
       "      <td>-0.050356</td>\n",
       "      <td>-1.717725</td>\n",
       "      <td>0.852467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>391</td>\n",
       "      <td>-0.060097</td>\n",
       "      <td>-1.707299</td>\n",
       "      <td>0.817981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>393</td>\n",
       "      <td>-0.041901</td>\n",
       "      <td>-1.686447</td>\n",
       "      <td>0.882398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394</td>\n",
       "      <td>-0.050985</td>\n",
       "      <td>-1.676020</td>\n",
       "      <td>0.850240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>396</td>\n",
       "      <td>-0.059913</td>\n",
       "      <td>-1.655168</td>\n",
       "      <td>0.818631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   range  logratio  std_range  std_logratio\n",
       "0    390 -0.050356  -1.717725      0.852467\n",
       "1    391 -0.060097  -1.707299      0.817981\n",
       "2    393 -0.041901  -1.686447      0.882398\n",
       "3    394 -0.050985  -1.676020      0.850240\n",
       "4    396 -0.059913  -1.655168      0.818631"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the LIDAR data below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 811
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.scatter(df.std_range, df.std_logratio, color=blue)\n",
    "\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xlabel(\"Standardized range\")\n",
    "\n",
    "ax.set_yticklabels([])\n",
    "ax.set_ylabel(\"Standardized log ratio\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data set has a two interesting properties that make it useful for illustrating dependent density regression.\n",
    "\n",
    "1. The relationship between range and log ratio is nonlinear, but has locally linear components.\n",
    "2. The observation noise is [heteroskedastic](https://en.wikipedia.org/wiki/Heteroscedasticity); that is, the magnitude of the variance varies with the range.\n",
    "\n",
    "The intuitive idea behind dependent density regression is to reduce the problem to many (related) density estimates, conditioned on fixed values of the predictors.  The following animation illustrates this intuition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (scatter_ax, hist_ax) = plt.subplots(ncols=2, figsize=(16, 6))\n",
    "\n",
    "scatter_ax.scatter(df.std_range, df.std_logratio, color=blue, zorder=2)\n",
    "\n",
    "scatter_ax.set_xticklabels([])\n",
    "scatter_ax.set_xlabel(\"Standardized range\")\n",
    "\n",
    "scatter_ax.set_yticklabels([])\n",
    "scatter_ax.set_ylabel(\"Standardized log ratio\")\n",
    "\n",
    "bins = np.linspace(df.std_range.min(), df.std_range.max(), 25)\n",
    "\n",
    "hist_ax.hist(df.std_logratio, bins=bins, color=\"k\", lw=0, alpha=0.25, label=\"All data\")\n",
    "\n",
    "hist_ax.set_xticklabels([])\n",
    "hist_ax.set_xlabel(\"Standardized log ratio\")\n",
    "\n",
    "hist_ax.set_yticklabels([])\n",
    "hist_ax.set_ylabel(\"Frequency\")\n",
    "\n",
    "hist_ax.legend(loc=2)\n",
    "\n",
    "endpoints = np.linspace(1.05 * df.std_range.min(), 1.05 * df.std_range.max(), 15)\n",
    "\n",
    "frame_artists = []\n",
    "\n",
    "for low, high in zip(endpoints[:-1], endpoints[2:]):\n",
    "    interval = scatter_ax.axvspan(low, high, color=\"k\", alpha=0.5, lw=0, zorder=1)\n",
    "    *_, bars = hist_ax.hist(\n",
    "        df[df.std_range.between(low, high)].std_logratio, bins=bins, color=\"k\", lw=0, alpha=0.5\n",
    "    )\n",
    "\n",
    "    frame_artists.append((interval,) + tuple(bars))\n",
    "\n",
    "animation = ani.ArtistAnimation(fig, frame_artists, interval=500, repeat_delay=3000, blit=True)\n",
    "plt.close()\n",
    "# prevent the intermediate figure from showing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=\"1600\" height=\"600\" controls autoplay loop>\n",
       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAADfA21kYXQAAAKfBgX//5vcRem9\n",
       "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTUyIC0gSC4yNjQvTVBFRy00IEFWQyBjb2RlYyAtIENv\n",
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       "OC4yOS4xMDA=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(animation.to_html5_video())"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we slice the data with a window sliding along the x-axis in the left plot, the empirical distribution of the y-values of the points in the window varies in the right plot.  An important aspect of this approach is that the density estimates that correspond to close values of the predictor are similar.\n",
    "\n",
    "In the previous example, we saw that a Dirichlet process estimates a probability density as a mixture model with infinitely many components.  In the case of normal component distributions,\n",
    "\n",
    "$$y \\sim \\sum_{i = 1}^{\\infty} w_i \\cdot N(\\mu_i, \\tau_i^{-1}),$$\n",
    "\n",
    "where the mixture weights, $w_1, w_2, \\ldots$, are generated by a [stick-breaking process](https://en.wikipedia.org/wiki/Dirichlet_process#The_stick-breaking_process).\n",
    "\n",
    "Dependent density regression generalizes this representation of the Dirichlet process mixture model by allowing the mixture weights and component means to vary conditioned on the value of the predictor, $x$.  That is,\n",
    "\n",
    "$$y\\ |\\ x \\sim \\sum_{i = 1}^{\\infty} w_i\\ |\\ x \\cdot N(\\mu_i\\ |\\ x, \\tau_i^{-1}).$$\n",
    "\n",
    "In this example, we will follow Chapter 23 of [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/) and use a probit stick-breaking process to determine the conditional mixture weights, $w_i\\ |\\ x$.  The probit stick-breaking process starts by defining\n",
    "\n",
    "$$v_i\\ |\\ x = \\Phi(\\alpha_i + \\beta_i x),$$\n",
    "\n",
    "where $\\Phi$ is the cumulative distribution function of the standard normal distribution.  We then obtain $w_i\\ |\\ x$ by applying the stick breaking process to $v_i\\ |\\ x$.  That is,\n",
    "\n",
    "$$w_i\\ |\\ x = v_i\\ |\\ x \\cdot \\prod_{j = 1}^{i - 1} (1 - v_j\\ |\\ x).$$\n",
    "\n",
    "For the LIDAR data set, we use independent normal priors $\\alpha_i \\sim N(0, 5^2)$ and $\\beta_i \\sim N(0, 5^2)$.  We now express this this model for the conditional mixture weights using `PyMC3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_cdf(z):\n",
    "    return 0.5 * (1 + tt.erf(z / np.sqrt(2)))\n",
    "\n",
    "\n",
    "def stick_breaking(v):\n",
    "    return v * tt.concatenate(\n",
    "        [tt.ones_like(v[:, :1]), tt.extra_ops.cumprod(1 - v, axis=1)[:, :-1]], axis=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = len(df)\n",
    "K = 20\n",
    "\n",
    "std_range = df.std_range.values[:, np.newaxis]\n",
    "std_logratio = df.std_logratio.values\n",
    "\n",
    "with pm.Model(coords={\"N\": np.arange(N), \"K\": np.arange(K) + 1, \"one\": [1]}) as model:\n",
    "    alpha = pm.Normal(\"alpha\", 0.0, 5.0, dims=\"K\")\n",
    "    beta = pm.Normal(\"beta\", 0.0, 5.0, dims=(\"one\", \"K\"))\n",
    "    x = pm.Data(\"x\", std_range)\n",
    "    v = norm_cdf(alpha + pm.math.dot(x, beta))\n",
    "    w = pm.Deterministic(\"w\", stick_breaking(v), dims=[\"N\", \"K\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have defined `x` as a `pm.Data` container in order to use `PyMC3`'s posterior prediction capabilities later.\n",
    "\n",
    "While the dependent density regression model theoretically has infinitely many components, we must truncate the model to finitely many components (in this case, twenty) in order to express it using `PyMC3`.  After sampling from the model, we will verify that truncation did not unduly influence our results.\n",
    "\n",
    "Since the LIDAR data seems to have several linear components, we use the linear models\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\mu_i\\ |\\ x\n",
    "    & \\sim \\gamma_i + \\delta_i x \\\\\n",
    "\\gamma_i\n",
    "    & \\sim N(0, 10^2) \\\\\n",
    "\\delta_i\n",
    "    & \\sim N(0, 10^2)\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "for the conditional component means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model:\n",
    "    gamma = pm.Normal(\"gamma\", 0.0, 10.0, dims=\"K\")\n",
    "    delta = pm.Normal(\"delta\", 0.0, 10.0, dims=(\"one\", \"K\"))\n",
    "    mu = pm.Deterministic(\"mu\", gamma + pm.math.dot(x, delta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we place the prior $\\tau_i \\sim \\textrm{Gamma}(1, 1)$ on the component precisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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   "source": [
    "with model:\n",
    "    tau = pm.Gamma(\"tau\", 1.0, 1.0, dims=\"K\")\n",
    "    y = pm.Data(\"y\", std_logratio)\n",
    "    obs = pm.NormalMixture(\"obs\", w, mu, tau=tau, observed=y)\n",
    "\n",
    "pm.model_to_graphviz(model)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now sample from the dependent density regression model."
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "        </style>\n",
       "      <progress value='30000' class='' max='30000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [30000/30000 02:56<00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='30000' class='' max='30000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [30000/30000 02:50<00:00 Sampling chain 1, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SAMPLES = 20000\n",
    "BURN = 10000\n",
    "\n",
    "with model:\n",
    "    step = pm.Metropolis()\n",
    "    trace = pm.sample(SAMPLES, tune=BURN, step=step, random_seed=SEED, return_inferencedata=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify that truncation did not unduly influence our results, we plot the largest posterior expected mixture weight for each component.  (In this model, each point has a mixture weight for each component, so we plot the maximum mixture weight for each component across all data points in order to judge if the component exerts any influence on the posterior.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 811
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "max_mixture_weights = trace.posterior[\"w\"].mean((\"chain\", \"draw\")).max(\"N\")\n",
    "ax.bar(max_mixture_weights.coords.to_index(), max_mixture_weights)\n",
    "\n",
    "ax.set_xlim(1 - 0.5, K + 0.5)\n",
    "ax.set_xticks(np.arange(0, K, 2) + 1)\n",
    "ax.set_xlabel(\"Mixture component\")\n",
    "\n",
    "ax.set_ylabel(\"Largest posterior expected\\nmixture weight\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since only three mixture components have appreciable posterior expected weight for any data point, we can be fairly certain that truncation did not unduly influence our results.  (If most components had appreciable posterior expected weight, truncation may have influenced the results, and we would have increased the number of components and sampled again.)\n",
    "\n",
    "Visually, it is reasonable that the LIDAR data has three linear components, so these posterior expected weights seem to have identified the structure of the data well.  We now sample from the posterior predictive distribution to get a better understand the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pymc3/sampling.py:1690: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n",
      "  \"samples parameter is smaller than nchains times ndraws, some draws \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='5000' class='' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [5000/5000 03:23<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PP_SAMPLES = 5000\n",
    "\n",
    "lidar_pp_x = np.linspace(std_range.min() - 0.05, std_range.max() + 0.05, 100)\n",
    "\n",
    "with model:\n",
    "    pm.set_data({\"x\": lidar_pp_x[:, np.newaxis]})\n",
    "    pp_trace = pm.sample_posterior_predictive(trace, PP_SAMPLES, random_seed=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we plot the posterior expected value and the 95% posterior credible interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 811
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.scatter(df.std_range, df.std_logratio, color=blue, zorder=10, label=None)\n",
    "\n",
    "low, high = np.percentile(pp_trace[\"obs\"], [2.5, 97.5], axis=0)\n",
    "ax.fill_between(\n",
    "    lidar_pp_x, low, high, color=\"k\", alpha=0.35, zorder=5, label=\"95% posterior credible interval\"\n",
    ")\n",
    "\n",
    "ax.plot(lidar_pp_x, pp_trace[\"obs\"].mean(axis=0), c=\"k\", zorder=6, label=\"Posterior expected value\")\n",
    "\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xlabel(\"Standardized range\")\n",
    "\n",
    "ax.set_yticklabels([])\n",
    "ax.set_ylabel(\"Standardized log ratio\")\n",
    "\n",
    "ax.legend(loc=1)\n",
    "ax.set_title(\"LIDAR Data\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has fit the linear components of the data well, and also accommodated its heteroskedasticity.  This flexibility, along with the ability to modularly specify the conditional mixture weights and conditional component densities, makes dependent density regression an extremely useful nonparametric Bayesian model.\n",
    "\n",
    "To learn more about dependent density regression and related models, consult [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/), [_Bayesian Nonparametric Data Analysis_](http://www.springer.com/us/book/9783319189673), or [_Bayesian Nonparametrics_](https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=bayesian+nonparametrics+book).\n",
    "\n",
    "This example first appeared [here](http://austinrochford.com/posts/2017-01-18-ddp-pymc3.html).\n",
    "\n",
    "Author: [Austin Rochford](https://github.com/AustinRochford/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sat Apr 24 2021\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.7.6\n",
      "IPython version      : 7.13.0\n",
      "\n",
      "numpy     : 1.17.5\n",
      "matplotlib: 3.2.1\n",
      "pymc3     : 3.11.2\n",
      "arviz     : 0.11.2\n",
      "theano    : 1.1.2\n",
      "pandas    : 1.1.5\n",
      "seaborn   : 0.10.0\n",
      "\n",
      "Watermark: 2.2.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}