init

beta-bernoulli.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beta-bernoulli Model in Stan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\begin{aligned}\n",
+    "\\theta &\\sim Beta(2,2) \\\\\n",
+    "x_i &\\sim Bernoulli(\\theta)\n",
+    "\\end{aligned}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pystan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "code = \"\"\"\n",
+    "data {\n",
+    "  int<lower=0> N;\n",
+    "  int<lower=0,upper=1> x[N];\n",
+    "}\n",
+    "parameters {\n",
+    "  real<lower=0,upper=1> theta;\n",
+    "}\n",
+    "model {\n",
+    "  theta ~ beta(2,2);\n",
+    "  for (i in 1:N)\n",
+    "    x[i] ~ bernoulli(theta);\n",
+    "}\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm = pystan.StanModel(model_code=code)\n",
+    "fit = sm.sampling(data={'N': 10,'x': [1,1,1,0,0,0,0,0,0,0]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "fit.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vectorized version \n",
+    "Stan supports [vectorization](https://mc-stan.org/docs/2_18/stan-users-guide/vectorization.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "code_vec = \"\"\"\n",
+    "data {\n",
+    "  int<lower=0> N;\n",
+    "  int<lower=0,upper=1> x[N];\n",
+    "}\n",
+    "parameters {\n",
+    "  real<lower=0,upper=1> theta;\n",
+    "}\n",
+    "model {\n",
+    "  theta ~ beta(2,2);\n",
+    "  x ~ bernoulli(theta);\n",
+    "}\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm_vec = pystan.StanModel(model_code=code_vec)\n",
+    "fit_vec = sm_vec.sampling(data={'N': 10,'x': [1,1,1,0,0,0,0,0,0,0]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_vec.plot();"
+   ]
+  }
+ ],
+ "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

binder/apt.txt

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+htop

binder/requirements.txt

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+pystan
+arviz