pymc3_examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Intialize random number generator\n",
    "np.random.seed(123)\n",
    "\n",
    "# True parameter values\n",
    "alpha, sigma = 1, 1\n",
    "beta = [1, 2.5]\n",
    "\n",
    "# Size of dataset\n",
    "size = 100\n",
    "\n",
    "# Predictor variable\n",
    "X1 = np.linspace(0, 1, size)\n",
    "X2 = np.linspace(0,.2, size)\n",
    "\n",
    "# Simulate outcome variable\n",
    "Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LF7N8FcD4mfAdQNE1Gg3NzS1obm5BjUYjtWPLLKvz1F5HV62eVLV6Mrf1YrXa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\nnFkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f830c5529d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, sharex=True, figsize=(10,4))\n",
    "axes[0].scatter(X1, Y)\n",
    "axes[1].scatter(X2, Y)\n",
    "axes[0].set_ylabel('Y'); axes[0].set_xlabel('X1'); axes[1].set_xlabel('X2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymc3 import Model, Normal, HalfNormal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basic_model = Model()\n",
    "\n",
    "with basic_model:\n",
    "\n",
    "    # Priors for unknown model parameters\n",
    "    alpha = Normal('alpha', mu=0, sd=10)\n",
    "    beta = Normal('beta', mu=0, sd=10, shape=2)\n",
    "    sigma = HalfNormal('sigma', sd=1)\n",
    "\n",
    "    # Expected value of outcome\n",
    "    mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "\n",
    "    # Likelihood (sampling distribution) of observations\n",
    "    Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "For compute_test_value, one input test value does not have the requested type.\n\nThe error when converting the test value to that variable type:\nWrong number of dimensions: expected 0, got 1 with shape (100,).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-c25c4c7ed7fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[1;31m# Likelihood (sampling distribution) of observations\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNormal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Y_obs'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/pymc3/distributions/distribution.pyc\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(cls, name, *args, **kwargs)\u001b[0m\n\u001b[0;32m     20\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'observed'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m             \u001b[0mdist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#for pickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/pymc3/model.pyc\u001b[0m in \u001b[0;36mVar\u001b[1;34m(self, name, dist, data)\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"transform\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m                 \u001b[0mvar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFreeRV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfree_RVs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/pymc3/model.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, type, owner, index, name, distribution, model)\u001b[0m\n\u001b[0;32m    436\u001b[0m             self.tag.test_value = np.ones(\n\u001b[0;32m    437\u001b[0m                 distribution.shape, distribution.dtype) * distribution.default()\n\u001b[1;32m--> 438\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp_elemwiset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    439\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/pymc3/distributions/continuous.pyc\u001b[0m in \u001b[0;36mlogp\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m    171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m         return bound(\n\u001b[1;32m--> 173\u001b[1;33m             \u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mtau\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtau\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mpi\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2.\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2.\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    174\u001b[0m             \u001b[0mtau\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m             \u001b[0msd\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/theano/tensor/var.pyc\u001b[0m in \u001b[0;36m__sub__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[1;31m# and the return value in that case\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbasic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msub\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    148\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mNotImplementedError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAsTensorError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    505\u001b[0m         \"\"\"\n\u001b[0;32m    506\u001b[0m         \u001b[0mreturn_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'return_list'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 507\u001b[1;33m         \u001b[0mnode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    509\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_test_value\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'off'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/theano/tensor/elemwise.pyc\u001b[0m in \u001b[0;36mmake_node\u001b[1;34m(self, *inputs)\u001b[0m\n\u001b[0;32m    543\u001b[0m                     \u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbroadcastable\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m                     \u001b[1;33m[\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mdifference\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m                     inplace=False)(input))\n\u001b[0m\u001b[0;32m    546\u001b[0m         \u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    515\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mins\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m                     \u001b[0mstorage_map\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_test_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m                     \u001b[0mcompute_map\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36m_get_test_value\u001b[1;34m(cls, v)\u001b[0m\n\u001b[0;32m    452\u001b[0m             \u001b[1;31m# ensure that the test value is correct\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 454\u001b[1;33m                 \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtag\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    455\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m                 \u001b[1;31m# Better error message.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/theano/tensor/type.pyc\u001b[0m in \u001b[0;36mfilter\u001b[1;34m(self, data, strict, allow_downcast)\u001b[0m\n\u001b[0;32m    167\u001b[0m             raise TypeError(\"Wrong number of dimensions: expected %s,\"\n\u001b[0;32m    168\u001b[0m                             \" got %s with shape %s.\" % (self.ndim, data.ndim,\n\u001b[1;32m--> 169\u001b[1;33m                                                         data.shape))\n\u001b[0m\u001b[0;32m    170\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maligned\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: For compute_test_value, one input test value does not have the requested type.\n\nThe error when converting the test value to that variable type:\nWrong number of dimensions: expected 0, got 1 with shape (100,)."
     ]
    }
   ],
   "source": [
    "basic_model = Model()\n",
    "\n",
    "with basic_model:\n",
    "\n",
    "    # Priors for unknown model parameters\n",
    "    alpha = Normal('alpha', mu=0, sd=10)\n",
    "    beta = Normal('beta', mu=0, sd=10, shape=2)\n",
    "    sigma = HalfNormal('sigma', sd=1)\n",
    "\n",
    "    # Expected value of outcome\n",
    "    mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "\n",
    "    # Likelihood (sampling distribution) of observations\n",
    "    Y = Normal('Y_obs', mu=mu, sd=sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "No model on context stack, which is needed to use the Normal('x', 0,1) syntax. Add a 'with model:' block",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-0fffde8373ce>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNormal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'alpha'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mbeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNormal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'beta'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msigma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mHalfNormal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sigma'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# Expected value of outcome\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/downey/anaconda/lib/python2.7/site-packages/pymc3/distributions/distribution.pyc\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(cls, name, *args, **kwargs)\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No model on context stack, which is needed to use the Normal('x', 0,1) syntax. Add a 'with model:' block\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: No model on context stack, which is needed to use the Normal('x', 0,1) syntax. Add a 'with model:' block"
     ]
    }
   ],
   "source": [
    "alpha = Normal('alpha', mu=0, sd=10)\n",
    "beta = Normal('beta', mu=0, sd=10, shape=2)\n",
    "sigma = HalfNormal('sigma', sd=1)\n",
    "\n",
    "# Expected value of outcome\n",
    "mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "\n",
    "# Likelihood (sampling distribution) of observations\n",
    "Y = Normal('Y_obs', mu=mu, sd=sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': array(1.01366409951285), 'beta': array([ 1.46791595,  0.29358319]), 'sigma_log': array(0.11928764983495602)}\n"
     ]
    }
   ],
   "source": [
    "from pymc3 import find_MAP\n",
    "\n",
    "map_estimate = find_MAP(model=basic_model)\n",
    "\n",
    "print(map_estimate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 5.4 sec"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/downey/anaconda/lib/python2.7/site-packages/theano/gof/cmodule.py:293: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
      "  rval = __import__(module_name, {}, {}, [module_name])\n"
     ]
    }
   ],
   "source": [
    "from scipy import optimize\n",
    "from pymc3 import NUTS, sample\n",
    "\n",
    "with basic_model:\n",
    "\n",
    "    # obtain starting values via MAP\n",
    "    start = find_MAP(fmin=optimize.fmin_powell)\n",
    "\n",
    "    # instantiate sampler\n",
    "    step = NUTS(scaling=start)\n",
    "\n",
    "    # draw 2000 posterior samples\n",
    "    trace = sample(2000, step, start=start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Fi6ozsoJk8F63Tz4p9zKlfQ4l/d0n9XRXQ7VLuO6NmTu1FzBRRE6vXiSHwzy0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gcWAecJ+qzheRK0TkCgAR2VVElgDXATeLyHu50PZWBCnCfknWXkXDb1/vOmcfETOKHJbXsHFj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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82f9e08190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymc3 import traceplot\n",
    "\n",
    "traceplot(trace);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "alpha:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.999            0.234            0.007            [0.535, 1.424]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.543          0.844          1.002          1.153          1.454\n",
      "\n",
      "\n",
      "beta:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  1.638            2.107            0.128            [-2.402, 5.976]\n",
      "  -0.436           10.226           0.622            [-21.629, 19.058]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -2.333         0.188          1.580          2.918          6.062\n",
      "  -21.700        -6.718         -0.462         6.751          19.007\n",
      "\n",
      "\n",
      "sigma_log:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.141            0.077            0.002            [-0.004, 0.293]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -0.001         0.087          0.140          0.191          0.300\n",
      "\n",
      "\n",
      "sigma:\n",
      "\n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  1.155            0.089            0.002            [0.989, 1.332]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.999          1.091          1.150          1.210          1.349\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pymc3 import summary\n",
    "\n",
    "summary(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pymc3.model.Model"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(basic_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pymc3.backends.base.MultiTrace"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__doc__',\n",
       " '__format__',\n",
       " '__getattr__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__len__',\n",
       " '__module__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_attrs',\n",
       " '_slice',\n",
       " '_straces',\n",
       " 'chains',\n",
       " 'get_values',\n",
       " 'nchains',\n",
       " 'point',\n",
       " 'varnames']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alpha', 'beta', 'sigma_log', 'sigma']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace.varnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.00911214,  1.00911214,  1.0349891 , ...,  1.17301994,\n",
       "        1.01888131,  0.94337901])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace.get_values('alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Var',\n",
       " 'Y_obs',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__doc__',\n",
       " '__enter__',\n",
       " '__exit__',\n",
       " '__format__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__module__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " 'add_random_variable',\n",
       " 'alpha',\n",
       " 'basic_RVs',\n",
       " 'beta',\n",
       " 'cont_vars',\n",
       " 'contexts',\n",
       " 'd2logp',\n",
       " 'deterministics',\n",
       " 'disc_vars',\n",
       " 'dlogp',\n",
       " 'fastd2logp',\n",
       " 'fastdlogp',\n",
       " 'fastfn',\n",
       " 'fastlogp',\n",
       " 'fn',\n",
       " 'free_RVs',\n",
       " 'get_context',\n",
       " 'get_contexts',\n",
       " 'logp',\n",
       " 'logp_elemwise',\n",
       " 'logpt',\n",
       " 'makefn',\n",
       " 'missing_values',\n",
       " 'model',\n",
       " 'named_vars',\n",
       " 'observed_RVs',\n",
       " 'potentials',\n",
       " 'profile',\n",
       " 'sigma_log',\n",
       " 'test_point',\n",
       " 'unobserved_RVs',\n",
       " 'vars']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(basic_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Y_obs]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basic_model.observed_RVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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    {
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       "[alpha, beta, sigma_log, sigma]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
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   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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