cookie_soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bayesian Statistics Made Simple\n",
    "===\n",
    "\n",
    "Code and exercises from my workshop on Bayesian statistics in Python.\n",
    "\n",
    "Copyright 2016 Allen Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we're running on Colab, install empiricaldist\n",
    "# https://pypi.org/project/empiricaldist/\n",
    "\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "\n",
    "if IN_COLAB:\n",
    "    !pip install empiricaldist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "sns.set_context('talk')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from empiricaldist import Pmf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Pmfs\n",
    "\n",
    "Create a Pmf object to represent a six-sided die."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "d6 = Pmf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Pmf is a map from possible outcomes to their probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in [1,2,3,4,5,6]:\n",
    "    d6[x] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially the probabilities don't add up to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "5    1\n",
       "6    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`normalize` adds up the probabilities and divides through.  The return value is the total probability before normalizing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6.normalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the Pmf is normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "1    0.166667\n",
       "2    0.166667\n",
       "3    0.166667\n",
       "4    0.166667\n",
       "5    0.166667\n",
       "6    0.166667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can compute its mean (which only works if it's normalized)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`choice` chooses a random values from the Pmf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 1, 3, 1, 4, 5, 1, 5, 4, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6.choice(size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`bar` plots the Pmf as a bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decorate_dice(title):\n",
    "    \"\"\"Labels the axes.\n",
    "    \n",
    "    title: string\n",
    "    \"\"\"\n",
    "    plt.xlabel('Outcome')\n",
    "    plt.ylabel('PMF')\n",
    "    plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d6.bar()\n",
    "decorate_dice('One die')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`d6.add_dist(d6)` creates a new `Pmf` that represents the sum of two six-sided dice."
   ]
  },
  {
   "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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.138889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.138889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "2     0.027778\n",
       "3     0.055556\n",
       "4     0.083333\n",
       "5     0.111111\n",
       "6     0.138889\n",
       "7     0.166667\n",
       "8     0.138889\n",
       "9     0.111111\n",
       "10    0.083333\n",
       "11    0.055556\n",
       "12    0.027778\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twice = d6.add_dist(d6)\n",
    "twice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1:**  Plot `twice` and compute its mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.999999999999998"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X7dZd9pGZd42rZ5Kk9Vav4fNP9aubC7qUrRlF25KkDYzL60iSGtfz8jqSJPWLEw4kSY0zfCRJjTN8JEmNM3wkSY0zfCRJjTN8JEmNM3wkSY0zfCRJjTN8JEmNM3wkSY0zfCRJjTN8JEmNM3wkSY0zfCRJjTN8JEmNM3wkSY0zfCRJjTN8JEmNW+trtDX5rHxwFUuXr+x7u9tvM4MZ0zbpe7vS+sZ/g2tn+KyHli5fyaHnXN/3dhceMZe5M7foe7vS+sZ/g2s38PCJiDcDxwEzgWXAyZn5hRHqbwqcAhwIbApcCxyVmbe31dkduK7L4d/IzH3713tJ0lgM9J5PRBwMXABcDuwPXA2cFxEHjXDYhcDBwLHAYcC2wFURsVlbnR2B3wIv7/h6X58/giRpDAY98jkZuCgz59fvL4uIzYETgYs7K9cRzeuB12XmpbXsOuBO4B2UERGU8Lk1M/s/7pUkjdvARj4RMROYBXypY9fFwJyI2K7LYa8FhoArWgWZuQK4hhJKLTsB3+9rhyVJfTPIkc+cus2O8jvqNigjms5j7sjMx7occwhARGwEvBi4PyJurn++BzgD+ERmrulP9yVJYzXI8Gndo+mcjzhUtzOGOabb/MWhtvovBKZRwutDwArgDcBptc7xY++yJKkfBhk+U+q2cyTSKl89zDHdRi5T2ur/DHgdcEtm3lPLroyIpwPHRsTpmTnUpQ1JUkMGGT6/rtvOEc70jv2dx8zsUj69Vb8Gy6Vd6nwDeDtlRHTjaDsrSeqfQU61bt3rmd1RPrtjf+cxMyNiSkf57Fb9iNghIt4ZEZ2PAU+r2/vH2F9JUp8MLHwy8w7KhILOZ3oOBG7PzLu6HHY58Axgr1ZBRGwF7AEsrkUvAD5NufTW7pB6vp+Mu/OSpHEZ9HM+JwCfj4gHgEuA/YA3AYfC48EyC1iamSsz89qIuBpYGBEfAH4JfBj4FfCZ2uYllMtq50bE1sBPgbfUtg90tpskDd5AVzjIzAWUh0P3BhYB84DDMvPCWmUfYAmwc9thbwS+BpwOLADuBv44Mx+obT5CGfUsosxs+yqwPXBAZn5lQj+QJKkngx75kJlnA2cPs28BJWDayx4A3la/hmvzfuDIvnVSktRXvs9HktQ4w0eS1DjDR5LUOMNHktQ4w0eS1DjDR5LUOMNHktQ4w0eS1DjDR5LUOMNHktQ4w0eS1DjDR5LUOMNHktQ4w0eS1DjDR5LUOMNHktQ4w0eS1DjDR5LUOMNHktQ4w0eS1Lipg+7AhmDlg6tYunxlX9vcfpsZzJi2SV/blDR5TbafM4ZPA5YuX8mh51zf1zYXHjGXuTO36GubkiavyfZzxstukqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxhk+kqTGGT6SpMYZPpKkxk0ddAci4s3AccBMYBlwcmZ+YYT6mwKnAAcCmwLXAkdl5u1tdaYCxwOHA1sANwHvy8wbJuZTSJJGY6Ajn4g4GLgAuBzYH7gaOC8iDhrhsAuBg4FjgcOAbYGrImKztjpnAEdTQuoQ4FFgcUTM7PdnkCSN3qBHPicDF2Xm/Pr9ZRGxOXAicHFn5YjYHXg98LrMvLSWXQfcCbwDOCUing8cCbwnMz9b61wO/BA4BnjnhH4iSdJaDWzkU0chs4Avdey6GJgTEdt1Oey1wBBwRasgM1cA11BCCeDVwMbt7Wbmw8AlbXUkSQM0Zc2aNQM5cUS8HvgGsGNmfr+t/KXAzbSNbtr2XQTMzsydO8rPAA7JzGdHxKnA2zNz844684FPAE/PzAdH2dfVwJTp06eP5rDHrVkDq/v833mjKVOYMqW58410zvX9fBN1Ts+3/v6d2RD+jq7N0NAQwJrM7DrIGeRlt9Y9mpUd5UN1O2OYYzrrt46Z0UMdgOnAqMIHWA1sNDQ01K1dSdKTzaD87OxqkOHTytPOqG6Vd+v0lC71W+Wre6gzXLsjysxB3xuTpPXKIGe7/bpuO0c40zv2dx7TbUQ0va3+SHWg+6hIktSgQYZP1u3sjvLZHfs7j5kZEZ1XIWe31U9g84h4Zpc6d2bmI2PsrySpTwYWPpl5B2WKdOczPQcCt2fmXV0Ouxx4BrBXqyAitgL2ABbXotZMuIPa6jwV2KetjiRpgAY22w0gIg4HPg98ijIVej/KcziHZuaFNVhmAUszc2U95irgJcAHgF8CH6asYrBDZj5Q6yygPFz6IeB2ygOnuwA719CTJA3QQFc4yMwFlIdD9wYWAfOAwzLzwlplH2AJ0D61+o3A14DTgQXA3cAft4KnOhL4LPBByooIU4HXGDyStG4Y6MhHkrRhclVrSVLjDB9JUuMMH0lS4wwfSVLjDB9JUuNcs2wdEBEbAUcA76K80fVe4KvA8Zk5NNKxk1FEfBl4SWZ2rm4xqUXEHsA/UB4N+BXltR5/k5m/GWjH+iQi3gEcBTwX+BFwSmZeMNhejV9E7AT8F7BdZt7dVv5a4CTgRZR/k2dl5scH08uxG+HzHUx5XnIO5e/rYuDYzLyviX458lk3fAA4i/KKif2BjwN/Afz7IDs1ESLircABg+5Hv0XEXMrqGvdQHpY+AXgr8LlB9qtfIuII4DOUv6NvoPygOr/+AJu0IiIoD7hP7Sh/RS2/jfJs4QXAaRHx/sY7OQ4jfL5DgIuAmyiryhxHeRfa4roizITzOZ8Bq+vU/QL4Yma+u638EGAh8NLMvGVQ/euniPhD4Fbgt8DD69PIJyKuqX+cl5lratm7Katr7JCZvxtY5/ogIv4TeCgzX91Wdi3wWGbuObiejU1ETKVcbfgYsArYHHhOa2QQEYuBTTNzbtsxp9Rjnl1fULnO6uHz3QL8LDP3aTtmN+B64IDMXDTRfXTkM3jTgfOBf+sov61uZzXbnQn1Ocr6fN8edEf6KSK2BF4FfKYVPACZ+anMnDXZg6d6Gk+8E6vlF5SlrSaj3YFTKVcZjm3fERFPo6wX2e0ty88AXtFEB8dppM83hTJyPafjmEZ/5njPZ8DqmnXv7bJr/7r9nwa7M2Ei4u3AyyjXz08fcHf6bQfK+6J+GREXAvsCj1J+oTh6tG/OXUedAZxbL7NdRnml/b6U9RMnox8AMzPzvrrGZLuZwCY8eWX91vJcAVw1sd0bt2E/X/0Fqdvlw0Z/5hg+66A6/P0gsCgzb1tb/XVdRDyP8grzt2Xm/eUy9Hplq7pdAHwF+FNgR+CjwDTg8IH0qr++SLkncFFb2XmZedqA+jMumXnvCLvH8pbldcpaPt+TRMQsyi+FN1OuTkw4w2cdExGvpNwgvBN4+4C7M251iP8vwDczs/MyxvriKXX7n2337a6sn/30iDghM388oL71y9col5uOpvyA2g34+4hYmZndRu6T2XBvWW4Z9duQ12URMYcSOI8Cb8rMRj6f4bMOqZMMFgA/BP4kM38x2B71xbspr8DYod4EhfqPu37/WPt9kkmq9RvxNzvKL6Ncc98BmLThU2d+7U0ZuS6oxddExK+AsyPi3Mz874F1sP+Ge8vyjI79k15EzAO+DPwG2DMzf9TUuZ1wsI6IiKMplzaWAHtk5vIBd6lfDgK2BJZTZt2sAg6j3NRcRZlSPtndXredU1RbI6LJHq7Pq9v/6Ci/tm63b7AvTfgR8Bije8vypFN/2b2M8lqalzd9id/wWQdExF9SfkO+iDLiWW9+s6K8W2nXjq9LKH/hdwW+Priu9c0PgJ8Ah3aUtyYeLGm8R/3V+mG7R0f5y+t2WXNdmXiZ+RAlWN9YL522HEgZ9dw4kI71UUTsTZlluwTYPTN/1nQfvOw2YBGxNXAm5YfXWcDOHTfk78jM+wfRt37IzCf9lhgRv6A85zPp/xFDmT0UEccCX4yI8ymXTl9GeXDvzMxcMcj+jVdm3hwRi4B/iogZwPcobwb+e+BbmfndgXZwYnyUMh15YX0z8iuAY4APTvap8/Uh0n+mXGo7Cdi+42fOT5sII8Nn8P4EeDrl0sZ1Xfb/OeU3FK3D6mvfH6b8QL4EuI+yysHJA+1Y/xwKHA/MB7amjHZOB04ZYJ8mTGZeGREHAh+hvGX5Z8Axk3F5nS52A7atf+42s+3vKOE7oVzhQJLUOO/5SJIaZ/hIkhpn+EiSGmf4SJIaZ/hIkhpn+EiSGudzPtI41Af23k15DmYOZSmdH1GWSjpnrKtV1IePf5uZv+1XX6V1iSMfaYwiYlvKUiunU5YL+hvgbykv5foH4KYYw/sjIuJ1lCVttlpbXWmycuQjjUFEPAX4KvB8YK/MvLJt91kRcQZlletvRcSLR7kky26UN2ZK6y1HPtLY/AVl/bb3dwQPAHW9s6OA7Shrgklq4/I60hhExHXATsBWdRXkbnWmAD8HhjLzhRGxDFiWmfM66j1eXhexbH/NxDWt+vWlXydQ3ii6CWWBz7/LzOva2toBOBGYR3nFw/8DPpaZi9rqXE1ZVPLc2l5Q7lO9H/gO5TLiwZQVub8MzG9/FXhEvLweN7cWLQGOy8wbRvhPJv0eRz7SKEXExpTXQXxvuOCBsto1cBXwgoh4do/Nn015FTeURTxPqud8AfBdSvCcBXwI2By4IiJ2rXV2Ba6nXLb7eK3zFOArEfFuft/OwOcp4XIs5Z1L/065VPj8euxiyisxPtD22V8DXEN51XRrAcrnAtdGxKt6/IyS93ykMdicMqro5YV/P6/bP+yl4cxcEhHfBw4AFmXmsrrro5TRzssy8w6AiFhIGbEcA7wJ+CTlFc+7Zubdtc5nKC+BOy0iLmx7Pcc2wJ9m5iW13irgU8DUzNy7lp0NvAp4LfCRiNgI+CxwA/BHmflYrXcWcAvl1SAv7eVzSo58pNFrvWDs0R7qruo4ZtTqD/3XA99sBQ9Afc367sB7I+JZlBHPv7aCp9Z5CDgNmAa8pq3Zh4BL277/Yd0+fnmujtyWUYIKSrDMrHWeGRFbRsSWte2vAztFxP8a6+fUhsWRjzR6Kyih8qwe6rZGPD8fsdbItgA25YnXdT8uM28FiIjdWkVdjv9B3T6vrewXmdkenq0/39dx7GM88UvqrLo9rX518xzKtHNpRI58pFGqI4L/AHaNiKcNV69OONgd+HFmjnSJbuO1nLK1f/UIdUYaWbX+nT/SVjbcqG2kGUitfvwdZRTV7eu2EY6XHufIRxqbf6XMKDuCcq+jmzdQLlOdWL9/jHKv6HERMZVys/9HI5zrfuBBYHbnjoh4P/Bsygw1KKssPKla3f50hHP0Ylnd/iYzF3f0Y1fKvbAHOw+SunHkI43NAsoU449FxGs7d0bETsA5wJ3AqbX4nrIrprVV3Q/oHD09VrcbAdTLY5cDr4+I57Sd45mUyQazMvMeymoLb22/71Ifhj0aeBi4Ykyf9Ak3UiZZvDciNm07xwzgIsrsuV7ug0mOfKSxyMzVEXEA8DXg0oj4MnAlJTjmAm8B7gLekJm/qYd9kTIj7dKIOJ8ykjkC+ElH8yvq9piI+FZmfo2ydM93gRvq7LKVwF9R7gUdV+u/t/bhvyLi08AQ8FbKw7DvzcxfjfMzr4qIv6YEzc0R8TnKxIW/otxPekvHfSRpWI58pDHKzHuBPSjPwvwh5fLaqcCOlEB4WWb+T9shnwaOp6x68EnKZbsDgFs7ml5IecbmbcAp9Vw/AF5Omeb8AcpDnsuB3VvnyMwlwCuBmygPjH6UEg77Z+Yn+/SZv0SZen035d7PiZQg3C8zv9iPc2jD4AoHkqTGOfKRJDXO8JEkNc7wkSQ1zvCRJDXO8JEkNc7wkSQ1zvCRJDXO8JEkNc7wkSQ1zvCRJDXu/wNyleI0S5J80QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "twice.bar()\n",
    "decorate_dice('Two dice')\n",
    "twice.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2:** Suppose I roll two dice and tell you the result is greater than 3.\n",
    "\n",
    "Plot the `Pmf` of the remaining possible outcomes and compute its mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.393939393939394"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "twice_gt3 = d6.add_dist(d6)\n",
    "twice_gt3[2] = 0\n",
    "twice_gt3[3] = 0\n",
    "twice_gt3.normalize()\n",
    "\n",
    "twice_gt3.bar()\n",
    "decorate_dice('Two dice, greater than 3')\n",
    "twice_gt3.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bonus exercise:** In Dungeons and Dragons, the amount of damage a [goblin](https://www.dndbeyond.com/monsters/goblin) can withstand is the sum of two six-sided dice.  The amount of damage you inflict with a [short sword](https://www.dndbeyond.com/equipment/shortsword) is determined by rolling one six-sided die.\n",
    "\n",
    "Suppose you are fighting a goblin and you have already inflicted 3 points of damage.  What is your probability of defeating the goblin with your next successful attack?\n",
    "\n",
    "Hint: `Pmf` provides comparator functions like `gt_dist` and `le_dist`, which compare two distributions and return a probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "damage = d6.add_dist(3)\n",
    "damage.bar()\n",
    "decorate_dice('Total Damage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "hit_points = d6.add_dist(d6)\n",
    "damage.ge_dist(hit_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The cookie problem\n",
    "\n",
    "`Pmf.from_seq` makes a `Pmf` object from a sequence of values.\n",
    "\n",
    "Here's how we can use it to create a `Pmf` with two equally likely hypotheses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bowl 1</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowl 2</th>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bowl 1    0.5\n",
       "Bowl 2    0.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cookie = Pmf.from_seq(['Bowl 1', 'Bowl 2'])\n",
    "cookie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can update each hypothesis with the likelihood of the data (a vanilla cookie)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.625"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cookie['Bowl 1'] *= 0.75\n",
    "cookie['Bowl 2'] *= 0.5\n",
    "cookie.normalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And display the posterior probabilities."
   ]
  },
  {
   "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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bowl 1</th>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowl 2</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bowl 1    0.6\n",
       "Bowl 2    0.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cookie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3:** Suppose we put the first cookie back, stir, choose again from the same bowl, and get a chocolate cookie.  \n",
    "\n",
    "What are the posterior probabilities after the second cookie?\n",
    "\n",
    "Hint: The posterior (after the first cookie) becomes the prior (before the second cookie)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bowl 1</th>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowl 2</th>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bowl 1    0.428571\n",
       "Bowl 2    0.571429\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "cookie['Bowl 1'] *= 0.25\n",
    "cookie['Bowl 2'] *= 0.5\n",
    "cookie.normalize()\n",
    "cookie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4:** Instead of doing two updates, what if we collapse the two pieces of data into one update?\n",
    "\n",
    "Re-initialize `Pmf` with two equally likely hypotheses and perform one update based on two pieces of data, a vanilla cookie and a chocolate cookie.\n",
    "\n",
    "The result should be the same regardless of how many updates you do (or the order of updates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>probs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bowl 1</th>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowl 2</th>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Bowl 1    0.428571\n",
       "Bowl 2    0.571429\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "cookie = Pmf.from_seq(['Bowl 1', 'Bowl 2'])\n",
    "cookie['Bowl 1'] *= 0.75 * 0.25\n",
    "cookie['Bowl 2'] *= 0.5 * 0.5\n",
    "cookie.normalize()\n",
    "cookie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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