solutions/height_soln.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Think Bayes\n",
    "\n",
    "This notebook presents code and exercises from Think Bayes, second edition.\n",
    "\n",
    "Copyright 2018 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from thinkbayes2 import Pmf, Cdf, Suite, Joint\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The height problem\n",
    "\n",
    "For adult male residents of the US, the mean and standard deviation of height are 178 cm and 7.7 cm.  For adult female residents the corresponding stats are 163 cm and 7.3 cm.  Suppose you learn that someone is 170 cm tall.  What is the probability that they are male? \n",
    "\n",
    "Run this analysis again for a range of observed heights and plot a curve that shows P(male) versus height.  What is the mathematical form of this function?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent the likelihood functions, I'll use `norm` from `scipy.stats`, which returns a \"frozen\" random variable (RV) that represents a normal distribution with given parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'male': <scipy.stats._distn_infrastructure.rv_frozen at 0x7fb1d67c3c50>,\n",
       " 'female': <scipy.stats._distn_infrastructure.rv_frozen at 0x7fb1d67c3e10>}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "dist_height = dict(male=norm(178, 7.7),\n",
    "                   female=norm(163, 7.3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a class that implements `Likelihood` using the frozen distributions.  Here's starter code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Height(Suite):\n",
    "    \n",
    "    def Likelihood(self, data, hypo):\n",
    "        \"\"\"\n",
    "        data: height in cm\n",
    "        hypo: 'male' or 'female'\n",
    "        \"\"\"\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "class Height(Suite):\n",
    "    \n",
    "    def Likelihood(self, data, hypo):\n",
    "        \"\"\"\n",
    "        data: height in cm\n",
    "        hypo: 'male' or 'female'\n",
    "        \"\"\"\n",
    "        height = data\n",
    "        return dist_height[hypo].pdf(height)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the prior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male 0.49\n",
      "female 0.51\n"
     ]
    }
   ],
   "source": [
    "suite = Height(dict(male=0.49, female=0.51))\n",
    "for hypo, prob in suite.Items():\n",
    "    print(hypo, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the update:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male 0.45677745168681627\n",
      "female 0.5432225483131837\n"
     ]
    }
   ],
   "source": [
    "suite.Update(170)\n",
    "for hypo, prob in suite.Items():\n",
    "    print(hypo, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the probability of being male as a function of height, for a range of values between 150 and 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def prob_male(height):\n",
    "    suite = Height(dict(male=0.49, female=0.51))\n",
    "    suite.Update(height)\n",
    "    return suite['male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "heights = np.linspace(130, 210)\n",
    "series = pd.Series(index=heights)\n",
    "\n",
    "for height in heights:\n",
    "    series[height] = prob_male(height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.plot(series)\n",
    "thinkplot.decorate(xlabel='Height (cm)',\n",
    "                   ylabel='Probability of being male')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are curious, you can derive the mathematical form of this curve from the PDF of the normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How tall is A?\n",
    "\n",
    "Suppose I choose two residents of the U.S. at random.  A is taller than B.  How tall is A?\n",
    "\n",
    "What if I tell you that A is taller than B by more than 5 cm.  How tall is A?\n",
    "\n",
    "For adult male residents of the US, the mean and standard deviation of height are 178 cm and 7.7 cm.  For adult female residents the corresponding stats are 163 cm and 7.3 cm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are distributions that represent the heights of men and women in the U.S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'male': <scipy.stats._distn_infrastructure.rv_frozen at 0x7fb1d67d7cf8>,\n",
       " 'female': <scipy.stats._distn_infrastructure.rv_frozen at 0x7fb1d67c3ba8>}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_height = dict(male=norm(178, 7.7),\n",
    "                   female=norm(163, 7.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs = np.linspace(130, 210)\n",
    "ps = dist_height['male'].pdf(hs)\n",
    "male_height_pmf = Pmf(dict(zip(hs, ps)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = dist_height['female'].pdf(hs)\n",
    "female_height_pmf = Pmf(dict(zip(hs, ps)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Pdf(male_height_pmf, label='Male')\n",
    "thinkplot.Pdf(female_height_pmf, label='Female')\n",
    "\n",
    "thinkplot.decorate(xlabel='Height (cm)',\n",
    "                   ylabel='PMF',\n",
    "                   title='Adult residents of the U.S.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `thinkbayes2.MakeMixture` to make a `Pmf` that represents the height of all residents of the U.S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170.34986539085568"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "from thinkbayes2 import MakeMixture\n",
    "\n",
    "metapmf = Pmf({male_height_pmf:0.49, female_height_pmf:0.51})\n",
    "mix = MakeMixture(metapmf)\n",
    "mix.Mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.Pdf(mix)\n",
    "\n",
    "thinkplot.decorate(xlabel='Height (cm)',\n",
    "                   ylabel='PMF',\n",
    "                   title='Adult residents of the U.S.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a class that inherits from Suite and Joint, and provides a Likelihood function that computes the probability of the data under a given hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "class Heights(Suite, Joint):\n",
    "    \n",
    "    def Likelihood(self, data, hypo):\n",
    "        \"\"\"\n",
    "        \n",
    "        data: who is taller, 'A' or 'B'?\n",
    "        hypo: h1, h2\n",
    "        \"\"\"\n",
    "        h1, h2 = hypo\n",
    "        if data == 'A':\n",
    "            return 1 if h1 > h2 else 0\n",
    "        else:\n",
    "            return 1 if h2 > h1 else 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a function that initializes your `Suite` with an appropriate prior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# We could also use MakeJoint for this\n",
    "\n",
    "def make_prior(A, B):\n",
    "    suite = Heights()\n",
    "\n",
    "    for h1, p1 in A.Items():\n",
    "        for h2, p2 in B.Items():\n",
    "            suite[h1, h2] = p1 * p2\n",
    "    return suite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000000022"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suite = make_prior(mix, mix)\n",
    "suite.Total()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Contour(suite)\n",
    "thinkplot.decorate(xlabel='B Height (cm)',\n",
    "                   ylabel='A Height (cm)',\n",
    "                   title='Posterior joint distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update your `Suite`, then plot the joint distribution and the marginal distribution, and compute the posterior means for `A` and `B`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47896674401037115"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "suite.Update(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "thinkplot.Contour(suite)\n",
    "\n",
    "thinkplot.decorate(xlabel='B Height (cm)',\n",
    "                   ylabel='A Height (cm)',\n",
    "                   title='Posterior joint distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164.05298129722948, 176.67506663725243)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IZDu+OkVkoTdtNQQve44V0NnZCcCMjHFDno08MiKcx++7zdV+68NjNDYFx+JdVaWkuomPz5Txfz8q4M9fzubZXx/nV7uKaGo1jsrgXbwmMxeRUOAF4B6gGDgsIptU9Yyj25eBalXNFJGNwA+Bz6rqb4Hf2teZB/xeVbMxGG6BHY7w3lCPnrpYv3Im72zLpry6jvrGFt7deZLPbOizorVf0treycHzleReqSP/ah01jW039NlztoLcklq+tDaDGeNvXQlpCFxCQ0OZN28eqkpoaCg//vGPWbVq1ZDew5sjqGVAgapeUNVW4FXgEY8+jwAv2dtvAuvkxpnrJ4ChT/JkGBFcKq3iwuVyAMLCQlm1aJpX7hMWFspn73c7pE3bT1LXcOulrn1FbVMbP/h9Li/tvsihC1U9OqcuKutb+Zct+bx+8DKt7Z3DaKXBH4iOjiY7O5sTJ07wD//wD3z7298e8nt400FNBC472sX2vh77qGo7UAMke/T5LMZBGW6RXY7R09K56S7FnTdYu3Q6E8eMBqwKve9sC6xBf21TG/+yJZ8r1U3d9o+KDGVR+mieWDmJ7316Dk/fNZVREaEAqMIHp67x/XfOcLGiwRdmG/yA2tpaEhMTh/y63swk0ZOG1zOTYK99RGQ50Kiqp3voh4g8DTwNMHny5Fs00xCsdHZ2dg/vLfNOeK+LkJAQNj64lH/95YcAbN55igfXzguIulG1TW388+Z8Sq9boz4R+MSiCSycMpq0xGhCQtwf1YmJ0UwfF8uvdheRU1wLQOn1Zv5+Ux6fWDSeBxeON3kJh5Gn/uuI1679X0/dPEzd1NTEwoULaW5uprS0lI8//njI7+/NEVQxMMnRTgNKbtZHRMKABKDKcXwjvYyeVPXnqrpEVZekpqYOidGG4OHUuRKqaxsBa1HtwhlpXr/nygVTSZ+YAkBbewdvfXjc6/ccLD05p6fWZvDw4glMTh7VzTl1kRgTwZ/fN53P3z6ZiDDrMdLRqbxztITN2aXDar/BN3SF+PLy8nj//fd58sknhzybuTcd1GFguohkiEgElrPZ5NFnE/BFe/sx4GO1f0IRCQEex5q7MhgGzI5D+a7tNbdNJyws1Ov3FBH+6KFlrva2A3l+XTOqpvFG5/SVO6eyPNMz0n4jIsKds8bw3U/NZtoY9yhxc3YpFXX++zMbhp6VK1dSUVFBeXn5kF7XayE+VW0XkWeBrUAo8AtVzRGR54AjqroJeBH4jYgUYI2cNjousQYoVtUL3rLRELw0Nbd2yzDu7fCek0WzJjF5fBKXSqtobWtnx6GzPLj25sUQfUVNYxv/vCWfqx7Oadm0pAFdZ0x8FH/50Ex+8PtcLlU20tahvH7wMl9bn+kNsw0e9BaGGy7y8vLo6OggObnvF5uB4NVs5qq6Bdjise87ju1mrFFST+fuAFZ40z5D8HLgRKEro8Ok8UmkTxzaD05viAj33zGXn72+C4D3d5/mgTVz/Wpeprapu3MKEXjqrqksmzow59RFaIjwuVWT+cc/WKmejhVd58yVWmZP7F+dLUPg0TUHBdZ6uZdeeonQ0KGNUphMEoagZMdhd3jvrmUzht05rFky3ZXpvKS8htPnPKdffYeq8v+2X+jmnL4yCOfURebYWFZkuq/xyv5LtHcY+Xmw0tHRQXZ2tktq/uCDDw75PYyDMgQdZVV1LocgwB23DX+oKSoynLuWubOlv7+7RyGqT9iRW05uiZU4V2zntHSQzqmLx5amERluPVZKrzezPXdo5yQMIwvjoAxBx64j51zbC2ZO8pnM+77Vc1zbh04VUXm93id2OCmrbeaNQ8Wu9ob544bMOQGMjongE4smuNqbjpVQ23Tzxb4GQ28YB2UIKlSVnQ71nrdSG/WHSeMSmZNpPaw7Vflwf67PbAHo7FR+sbPIlfVhQmIUDy+e0MdZA2f9nDGMTbAWRDe1dvDW4StDfg8DQy7p9gaDtdE4KENQce5iGSV2SffIiHCWzU/3qT3OUdRH+3J9WtDwo5xrFFyzRnEhAl9ak0F46NA/AsJCQ9i4wr1wfs/ZCi6U+X70GExERUVRWVnp105KVamsrCQqKuqWr+FVFZ/BMNzsOOTOHLFq0VQiI8J9aA0sn5fO6LhRXK9rpLq2kUOni1i10Dv5AHujpLqJt464RzIPLhxPeqr3Qp/zJiWwYHICJy5ZLwuv7L/MXz8806+UjIFMWloaxcXFQ77uaKiJiooiLe3WF8gbB2UIGlSV/Sfcy+acIgVfERYWyvpVs3hz61EAtu7JGXYH1dGp/GJXIe0d1tv25ORRPLhwvNfv+9kVk8gprqW9Uyksb2DvuUpWZ6V4/b4jgfDwcDIyMnxthtcxIT5D0FBYXEFtvZXoND42mtnTvP8Q7g/3rJxFiD1yOH2uhMtXq4f1/u+fvEpRuZXyKSxE+NLadMK8ENrzZEx8FPfNH+dq//ehYhpbTLVhQ/8xDsoQNGTnudVpC2ak+U04KSUxlmXz0l3tD/YOX8Xdy5WN/OGYew3Ww7dNIC1p1LDd/4EF40iMscKsdc3t7C+oHLZ7GwIf46AMQUN2nru6y8KZ3k8MOxCcYonth87S3OJ96XV7Rye/2FlIe6cV2ps6Job75o3r46yhJTI8lAcWuEeye88aB2XoP8ZBGYKC5pY28gqvutrzhyFz+UCYlzWRCXap+abm1m5rtbzFh6evcbnKCnmGhwpfWpNBaA+Zyb3N8mlJhIda971U2cilysZht8EQmBgHZQgKTheU0GGn1Zk8PsnvajCJSLdR1Hu7T3tVIlzT2Ma7jrIXjy6ZyLjRty73HQyjIsO4LcNdzG5PfoVP7DAEHsZBGYKCE93Ce5N66ek77lo+g3C75Mel0iryC6957V5vHSmmpc1y2ONHR7Fuzliv3as/3O5Q7x04X2lKxBv6hXFQhqAgO9fhoGb5p4OKiY5kzZLprvZWL4klCssbus31PLFysk9Ce05mjo8jOTYCgMaWDrIvXfepPYbAwDgoQ8BTVlXnyh4RHhbKrKnDKwQYCPeumu3a3p99YciLGaoqr+6/5GovnDLaL0peiAirZ7hHUXtNmM/QD4yDMgQ8zvDenMwJRIT77/rzaZNTmTzeSs7a1t7B3mPnh/T6h85Xcb6sAbDWPH1muf+IRVZmJtOl/D9TUktlvam6a+gd46AMAY9z/ZO/zj91ISKsXznL1f5oCBPItrR18OZh9+9i/dyxjIn3jTCiJ1LiIpk1wRrNqcK+c0ZybugdrzooEdkgIvkiUiAi3+rheKSIvGYfPygi6Y5j80Vkv4jkiMgpEfGfT5rBb+js7ORkvmOBrp+tf+qJNUumE2pncjh/uZyLJUPzoH7v5FWqG6z1VfHRYcOSzmigOFMd7T1b4dfJTg2+x2sOSkRCgReA+4HZwBMiMtuj25eBalXNBJ4HfmifGwa8DDyjqnOAOwFTVMZwAwWXymlsbgUgKSGGSeMS+zjD98TFRLF8vjuP2rYDeYO+ZkVdC1tPuteBPbpkItERQ1t+eyhYOGU0o2y7KupaySut87FFBn/GmyOoZUCBql5Q1VbgVeARjz6PAC/Z228C68TKT3MvcFJVTwCoaqWq+q5OgcFvcWaPWDDTf9Ib9YUzzLfz8Fna2gb37/3moWLa7GSwU1JG+W1S1oiwEJY7ysLvPWvEEoab400HNRG47GgX2/t67KOq7UANkAxkASoiW0XkmIj8pRftNAQw3eafZvj3/JOT+VkTSU2MA6C+sYVDp4tu+Vr5pXUcKXQnoH1i5WS/dtRO53m0sNokkDXcFG86qJ4+IZ4B55v1CQNWA39kf39URNbdcAORp0XkiIgc8fe6KIahp6GphXNF1mJXAebP8Hz/8V9EhLuWu8uBbLtFsURnp/LaAfd74LKpSWSOjR20fd5kSkoMk5OthLVtHcqhC1U+tsjgr3jTQRUDzlfaNKDkZn3seacEoMrev1NVK1S1EdgCLPa8gar+XFWXqOqS1NRUL/wIBn/m1NkrdNqT7FMnpRIfG+1jiwbG3ctnuN7QTuYXU1Y18PmYg+erXLntwkOFx5b5v0gE4PasZNf2HhPmM9wEbzqow8B0EckQkQhgI7DJo88m4Iv29mPAx2rJerYC80VklO241gJnvGirIQA5kR848vKeSE2KcyW1VWD7wfwBnd/a3snbR91Vcu+bP44kO1uDv7N8WjJhdnaLovJGiqtMAlnDjXjNQdlzSs9iOZtc4HVVzRGR50TkYbvbi0CyiBQA3wS+ZZ9bDfwblpPLBo6p6mZv2WoIPFS1W3qjQJCX98Q6h1hi+8H8Acmud+aVU1VvKRjjosKGvZTGYIiNCmPhlNGutinDYegJry65V9UtWOE5577vOLabgcdvcu7LWFJzg+EGrlbUukJikRHhzEj3bTLUW2XZ3HRiR0VS39hCeXUdJ89eYUE/SoU0trTz7nF3xPyhReP9UlbeG6tnpLjEHfsLKvn00onDUunXEDiY/wZDQHLCod6bN30CYWGB9XDuIjw8lLVLs1zt/maWeP/kVRpaLGl6SlwEa2cG3hzs7Anxrmq79c3tnLtW72OLDP6GcVCGgMRz/VMgs27FTNf2wZOF1DU099q/uqGVD0+7S3V8aklaQI48QkKkW5gv+6LJcG7oTuD9VxtGPO3tHZw86xYHBKJAwsmUCclMm2SNgDo6OvustvuHYyWuRbmTk0exdKr/Z8+4GYumuG3PvnjdpD4ydMM4KEPAcfZiGS2tVuar1MQ4xtul1AMZzwSyN3tQl15vYrdDlv3YssDJntETWeNiXXNnlfWtXKlu8rFFBn/COChDwNG9OGFgP6C7uH3xtG7Vds9f6nnh+VtHrtDlu2ZPjPeLWk+DISw0hLlp7p/BhPkMToyDMgQcpwvc6rUFAZTeqDdioiNZtWiaq73t4I0JZM9fq+d4kfsB/umlgZM5ozcWTnbMQ5lKuwYHxkEZAoqW1jYKLpW52rOn+V9JiVvFGebbdeQczS3uBP6q2q3W07KpSUxJiRlW+7zF3EkJrpL0ReWNVDe0+tgig79gHJQhoDhbVEZHRycAaWMTSYgLrPRGvTFr6jgm2PNpzS1t7M++4Dp28nIN565aMuywEOHRJcExegKIiQxj+jh3/sATZhRlsDEOyhBQ5Jx3h/fmZE7woSVDj4iwfpW7ZNqH9pooVeWtw27V4tpZqaTGRw67fd5kkZGbG3rAOChDQHGmoNS1HUzhvS7uXJpFSIj1scwvvMrlq9UcvlDtUrdFhof4ZaXcwbLAMQ+VV1JHc6sp/2YwDsoQQLS1dXC2yL1AdXZm8D2oE+KiWTZ3iqv94b4z/P6Ye/S0fs5Y4qPDfWGaV0mJiyQtyQrXtncqOVdqfWyRwR8wDsoQMBRcKqOt3XqzHpcST1JCcIgEPHEmkP39wSKuXrcyS4yKCOXeeYGZc7A/OLNKHL9Y3UtPw0jBOChDwJBz3hneC675JycLZ6aRkhiLAiUt4VTXWqUo7pk3lphIr+Z39inOMN+pyzV0dJqsEiMd46AMAUOuw0HNCcLwXhchISHcvXwmdUTRJqFUVNcTExnK+jnBO3oCSE8ZxehRVviyoaWDApM8dsRjHJQhIOjo6CT3wlVXe3aQKfg8WbNkOtfFKote29DMyqkJAVdOY6CICAuMms/gwDgoQ0BQWFzhyr+XkhjLmKQ4H1vkXfLKWxgVZ82xhWonrVUjoyy6M6vEiUsmeexIxzgoQ0DQff4peMN7YJVy35xdSkqitXh1tDay+3A+nZ2dPrbM+8ycEEdkuPVYKqttofR676VHDMGNVx2UiGwQkXwRKRCRb/VwPFJEXrOPHxSRdHt/uog0iUi2/fVTb9pp8H/OFATvAl1PduaVc72xjdFxo4gOE+JpoqqmgeOOJLnBSnhoCHPT3NnpTZhvZOM1ByUiocALwP3AbOAJEZnt0e3LQLWqZgLPAz90HDuvqgvtr2e8ZafB/+ns7OSMYwQ1K4hHUC1tHWzJtn5WEeHumcmuD2l/q+0GOiZ5rKELb46glgEFqnpBVVuBV4FHPPo8Arxkb78JrJNgqJ1gGFIulVbR2GwlEE2Ii3blqwtGtueWU9fcDkBiTDhfeWCe69iR0xddkvNgZt6kBOzcsRSWN1DT2NZktuxyAAAgAElEQVT7CYagxZsOaiLgjEkU2/t67KOq7UANkGwfyxCR4yKyU0Tu8KKdBj8np6D7+qdgfYdpbu3gvRPun/WhhRNIH5/ErKnWiLFTle0H831l3rARGxXG9HGWCEYVTl42o6iRijcdVE9PEU9Jzs36lAKTVXUR8E3gdyJyQ2U2EXlaRI6IyJHy8p4LvBkCn+7zT8Eb3tt2poyGFitTRnJsBLdnWe9q96xyZ5bYduDm1XaDiW5ZJYqMgxqpeNNBFQPOanJpQMnN+ohIGJAAVKlqi6pWAqjqUeA8kOV5A1X9uaouUdUlqampXvgRDL5GVTnjXP8UpBkkWto6+PC0O8/gJxZPICzU+niuWJDBqKgIAK5W1HLq7JUerxFMLJjsDuPmldbR2h78CkbDjXjTQR0GpotIhohEABuBTR59NgFftLcfAz5WVRWRVFtkgYhMBaYDFzCMOIqvXae23srkHTsqksnjE31skXfYnV9BvT33lBwbwYppSa5jkRHhrF3qfj/buvfMsNs33IyJj2KsXVKktb3TZJUYoXjNQdlzSs8CW4Fc4HVVzRGR50TkYbvbi0CyiBRghfK6pOhrgJMicgJLPPGMqlZ5y1aD/5Lrsf4pGOef2js62XrKPUrcMH+ca/TUxb23uwWwh04VUVXTMGz2+Yq5k9yjqNPFNT60xOArvLoOSlW3qGqWqk5T1R/Y+76jqpvs7WZVfVxVM1V1mapesPf/t6rOUdUFqrpYVf/gTTsN/stpx/xTsIb3DpyvorrBUqrFRYVxe1bKDX0mj09yLVDu7OwcEZLzOWnuaWfjoEYmJpOEwW9R1RtGUMFGZ6d2U+7dM28sEWE9fyzvu32Oa/uj/bl0dAT3vMyMcXGEhVoj5pLqZqrqW31skWG4MQ7K4Ldcrah1hbKiIsPJSEvu44zA49jFaq7VtABWvac7Z95c7LN8fgbxsVZRv8rrDRw9c2lYbPQVkeGhZI1z51zMuWJGUSMN46AMfotz9DRr6jhXKfRgQVXZku2ee7pr9hhG9VLvKTw8lHXLZ7jaH+zN8ap9/sBcR5jv1GXjoEYawfWJNwQVwV6gMOdKLZcqrcwQ4aHCujlj+jznnttnuxYPZude5mpFcJdGd+blyyupM0UMRxjGQRn8lmBfoNuVcw9gzcxU4qPD+zxnbHI8C2dZywsV+GhfcEvOx4+OIjHG+r00tnZQWB786kWDG+OgDH5JRXU9ZVV1AESEhzFtUnAtxC64Vs/Zq9bantAQ4d55/a+We69TLHEgj7a2jiG3z18QEeY5RlGnTZhvRGEclMEvOXPePXqakTGWsLDgqibrHD0tn5ZEcmxkv89dMmcyyaOtYoZ1Dc0cPFk45Pb5E3OcDsoIJUYUxkEZ/JLuCWKDK7xXXNXISXskIAL3Lxg3oPNDQkK4Z5V74e77e4JbLDFrQpwru3lReSO1TSa7+UjBOCiDXxLMBQqdyr1FU0YzfnT0gK+xbsVMl6ox90IpF0uCN9HKqMgwpo6JdbXPXAluYYjBjXFQBr+juraRknJrhBEaGsL0KX2r2wKFstpmDhe6nckDC25tdJiUEMOyeemu9odBLpaYZ9IejUiMgzL4Hc7quVlTxhIRfvO1QYHGtpwyuqplzJ4YT3pqzC1fa8Nqt1hix+GzNLcEb+hrzkT3eqic4toRUXLEYByUwQ/JORec8vLGlnb2nK1wtTfMH9jckydzp09wVRduam5l99Fzg7qePzMlZRRxUdaLSl1zOxcrgr+ysME4KIMfcuaCQyARRPNP+85V0tJm5c8bPzqKWRPi+jijd0Skm+T8/T1ngnZkISLdksfmmHmoEYFxUAa/ora+icul1hxNSEgIM9L7vz7In+nsVD4+U+Zqr5szZkhKh9y5LItwW4JfdKWC/MJrfZwRuDizSpi0RyMD46AMfoVz/mnapBSiIvvOrhAInLpcQ1mtOynsysyhSXwbFxPFmiXTXe3Nu04NyXX9kdkT4+ny6RfK6mlsafetQQavYxyUwa/IPe+WYM8NovDeNsfo6Y6ZKUSGD93C4wfWzHVtHzhRGLTFDOOjw5mSMgqAToXckjofW2TwNsZBGfyKbgUKg8RBXalucq3dEYG7Zg2tbD59YgqzprqLGQZzSfg5E91hvhwjNw96vOqgRGSDiOSLSIGIfKuH45Ei8pp9/KCIpHscnywi9SLyF9600+AfNDS1cPGKpXITYGbG4FRu/sK2HPe80KIpo0mJ639ao/5yv2MU9eG+M7S3B2d+vm7lN4prglYUYrDo1UGJyK8c218cyIVFJBR4AbgfmA08ISKzPbp9GahW1UzgeeCHHsefB94byH0NgUvuhat0PW4yJqUyKjrCp/YMBfXN7ew/V+lqr5vjHdHH8nnpJCVYa6pq6prYn33BK/fxNVPHxDIqwgqPVje0UXq92ccWGbxJXyOoBY7tbwzw2suAAlW9oKqtwKvAIx59HgFesrffBNaJLW0SkU8CF4DgTjRmcNEtvVGQ5N/bnV9OW4fldiclRZM1LraPM26NsLBQ7r3d/f4XrGKJ0BBhpkOeb9R8wU1fDmow4+eJwGVHu9je12MfVW0HaoBkEYkB/gr43iDubwgwuiWIDYL5pw5PafncsUMiLb8Z966aTWio9ZE+d7GMgotlfZwRmDjTHpn1UMFNXw4qTUT+Q0T+07Ht+urj3J4+iZ4O72Z9vgc8r6r1vd5A5GkROSIiR8rLy/swx+DPNDW3cuGy9TcUrBLvgU72xetUN1jph2Kjwlg2Ncmr90uIi+b2RdNc7S27T3v1fr7CKZQ4W1pHSxDXwxrp9OWg/jdwFDji2HZ+9UYxMMnRTgNKbtZHRMKABKAKWA78k4gUAX8O/LWIPOt5A1X9uaouUdUlqanBVdBupJFXeI1Oe8J70vgk4mKifGzR4PnIIY5YOzOViDDvi2adkvM9xwqoqWvy+j2Hm6TYCNKSrAzw7Z1KnpGbBy29ZuFU1Zd6O94Hh4HpIpIBXAE2Ap/z6LMJ+CKwH3gM+FgtWc4dXR1E5LtAvar+eBC2GPycXMcC3bnTAz+8d6mykXOOirl3zhqeF6jpU8aSOXkMBZfK6Ojo5MP9uTx27+JhufdwMjctgeIqy/meLK5hwZTRPrbI4A36UvFt6u2rt3PtOaVnga1ALvC6quaIyHMi8rDd7UWsOacC4JvADVJ0w8jAuf5pVhAIJD467R493ZaeSGLM8CkSnaOoD/bm0NHROWz3Hi6ccvPTl43cPFjpq47BSiwRwyvAQXqeM7opqroF2OKx7zuO7Wbg8T6u8d2B3NMQeLS0tlFwyT2hP2daYI+g6prbOHTBXfNp/dzhrWe1auE0fvXOfmrrm6i83sDBU4WsWjit7xMDiMyxsUSFh9Dc1kllfSul15uZkDjwwo8G/6avoPg44K+BucCPgHuAClXdqao7vW2cYWRwtqjM9ZafNjaRhLjAftDsO1tJuy0tT08d1a0a7HAQHh7Kvatmudrv7Qo+sURYaAizHTWiTBHD4KRXB6WqHar6vqp+EVgBFAA7ROTPhsU6w4gg57wzvVFgh/dUlV35bkXp2pm+Ee/ce/tsQmxJ+5nzpVwsqezjjMCje5VdIzcPRvqUFdnpiD4FvAz8KfAfwFveNswwcjjjWP8U6OG9s1fruVZjZS2PCg/xurT8ZiSPjmX5gqmu9uadwbdw18jNg5++RBIvAfuAxcD3VHWpqn5fVa8Mi3WGoKetrYOzRW5BQaCPoHbluUdPKzKThzRr+UB50CGW2HXkHHUNwZUWyMjNg5++RlBfALKw0hztF5Fa+6tORMyY2jBoCi6V0WYnNh2XEu/KJxeI1De3c7Sw2tVe46PwXhczp44jfWIKAG3tHXy4L9en9niDbkUMzTxU0NHXHFSIqsY5vuLtrzhVje/tXIOhP+Q41j/NDvDw3v6CSto73eKIycmjfGqPiPDQ2nmu9vt7TgddlvNu2c2N3Dzo6CvEFyUify4iP7bTCvUlSzcYBkTOOUeC2AAO76kqO/N8L47wZPXiTOJjrTCYJTkv8q1BQ0yX3Bxwyc0NwUNfIb6XgCXAKeAB4F+9bpFhxNDe3kF+t/mnwB1BnbtWz1X74RjpQ3GEJ+HhHlnOg0wsYeTmwU1fDmq2qn5eVX+GlYrojj76Gwz9puBSOS2tVjLV1MQ4xiTF9XGG/+IURyyfluRTcYQnG1bPcWU5zy+8GnRZzo3cPHjpy0G1dW3YqYsMhiEjO99djWVelmcllsChvrmdIw5xhL+E97pIjB/VLct5sNWKMnLz4KXPgoVO5R4w36j4DEPFyXz3aoUFM9N8aMngOFDgzhwxJWUUU1L8T4n40Nr5ru29x89TVdPgQ2uGFiM3D176UvGFeij3woyKzzAUNDS1cM6efxJgfoCOoDzFEWtm+NfoqYtpk1OZkWHV2Oro6GTr3jM+tmhoMXLz4MT7BWoMhh44dfaKq/5TxqRUl9Is0Dhf1uBSjkWGh7B8mn+II3riQYfk/IO9Z2gLolCYkZsHJ8ZBGXyCM7y3cEbghveco6flU5OIivAfcYQnK+ZnkDzaCj/W1jex51iBjy0aOjzl5ldrjNw8GDAOyuATTjgEEvMD1EE1tLRzxFFW4w4/E0d4EhoawobV7vRH7+48FTQjDU+5+anLJswXDBgHZRh2rlbUcrXC0thEhIcx054bCTQOFFTSZosjJiePIj3Ft5kj+sM9q2YRHmaN8oquVJB74aqPLRo6jNw8+DAOyjDsnMwvdm3PyRxPuB+tGeovqsquvApXe83MFEQGVM/TJ8TFRHHnsixXe/OOkz60ZmgxcvPgwzgow7BzIs8d3lswY5IPLbl1CssbuFLdBEBEWAjLpyX72KL+88Aat1ji4MlCyqqCQ5btKTfPNXLzgMerDkpENohIvogUiMi3ejgeKSKv2ccPiki6vX+ZiGTbXydE5FFv2mkYPjo6Ojl51iGQmBWYDmrPWffoaUlGItF+LI7wZPL4JOZnWfN+SnBV3J3vCPMdK6rupachEPCagxKRUOAF4H5gNvCEiMz26PZloFpVM4HngR/a+08DS1R1IbAB+JlJVBscnL9cTmNzK2BlOEgbO9rHFg2clvYODl9wP/xWz0jxoTW3xoN3ukdRH+3PpbmlrZfegcPCKe7/pxOXrtPRGRwikJGKN0dQy4ACVb2gqq3Aq8AjHn0ewUpIC/AmsE5ERFUbHamVorBe9AxBQLYzvDdzUkDM23hytLCaplZrfmNsfCTTx8b62KKBc9vsyUxItUYbjc2tbDuQ52OLhoaM1BgSY8IBaGjp4OxVE+YLZLzpoCYClx3tYntfj31sh1QDJAOIyHIRycHKpP5MT7kA7RIgR0TkSHl5uedhgx9ywiGQWDQzQMN7+e7w3u0zAkMc4YmI8KAj/dGWXafo7Oz0oUVDg4h0G0VlX7zuQ2sMg8WbDqqnT63nSOimfVT1oKrOAZYC3xaRqBs6qv5cVZeo6pLUVP9eg2KAxqZWzha6y2sEYoLYstpmzl6tByBEYFVm4IgjPLlzWRYx0ZGAJf0/knPJxxYNDYvTE13bx4qqg2at10jEmw6qGHC+IqcBJTfrY88xJQBVzg6qmgs0AHMxBDSnC0pc6Y3SJ6aQEBd46Y2co6d5kxIYHRPhQ2sGR1RkOPeumuVq/2H7CR9aM3RkjYsjJtISrVQ3tFFU0ehjiwy3ijcd1GFguohkiEgEsBHY5NFnE/BFe/sx4GNVVfucMAARmQLMAIq8aKthGHDKyxcGYPbyjk5l37lKV3t1VuCJIzzZcMdcQkKsx8CZ86UUFlf0cYb/ExoiLJjsDvMdN2q+gMVrDsqeM3oW2ArkAq+rao6IPCciD9vdXgSSRaQA+CbQJUVfDZwQkWzgbeBrqhr4n5wRjnP+aUEApjc6XVzD9UZL7RYfHdYtc0GgkpIYy8qFU13tTUEyiuoW5jPzUAGLV6XbqroF2OKx7zuO7Wbg8R7O+w3wG2/aZhheyqrqKC238qOFh4Uyc2rgpTdyrn1aOT2ZsNDgWOf+8J3z2Wsnjt17/DxfeHgFSQn+V9NqIMyeGE9EWAit7Z1cvd5M6fUmxo8OvJDySCc4PmEGv8cZ3puTOYGI8MBa1lbb1MbJS+4EpMEQ3usic8qYbrWi3t+d42OLBk9EWEi3EhzHi8woKhAxDsowLJwI8Oq5+89VuhZ9Zo6NDbq38YccC3e37s2hte2GVR0Bx6IpzjCfmYcKRIyDMnidzs7ObgliAy3/nqp2C+/dnhW40vKbsXxeBqmJcQDUN7aw49BZH1s0eOZPTiA0xFrJUlTeSFV9q48tMgwU46AMXuf85XIamloAK73R5PGJfZzhXxSWd6+auzTDf6vm3iqhoSE8sNa9kmNzENSKiokMY8b4OFf7uBlFBRzGQRm8jjO8N39GWsBlXtjtWPu0NMO/q+YOhnUrZhIZYaUJKr5WTXZecR9n+D+LHFkljhs1X8BhHJTB63QvrxFY808tbR0cclTNDcTEsP0lJjqS9StnutrvBkGtKKeDOltaR31z4M+tjSSMgzJ4lZq6JvIcVVsDrbz7kcJqWtqsHHVjEyKZNiaw5dd98cCaea78Y9l5l7lYUtlrf39ndEyE62/WqVaGc0PgYByUwascPl3kSm80I2McifH+XxbdiTO8d8eM1IALTw6UcSnxLJ+f4Wr//uPAX7i7yLFo14T5AgvjoAxeZX/2Bdf2ygVTe+npf1ypaqLgmpUYNjREWDU9+NR7PfHIuoWu7d1HC6iorvehNYNncbo7zJdTXGNKwQcQxkEZvEZdQ3O36rkrFmT00tv/2JXvLuGyaMpo4qPDfWjN8JGVPtaV6aOzs5PNO0/52KLBMSY+iomJ1rq1tg7ldHGtjy0y9BfjoAxe4/CpIleNoelTxpCaFNfHGf5DS3sH+x2JYdfOHFnlXD7pGEV9sO+Ma5lAoLIo3anmM3LzQME4KIPX2H/CEd5bOM2HlgycIxeqabSr5o6Jj2TmhMBxrkPBkjlTmDjGeqg3t7Txwd4zPrZocDiTx568VENbR+AXZxwJGAdl8AoNTS3dspcHXHgvzx3euyNAq+YOBhHhkXULXO3NO0/R3h64czeTkqJJibNqdzW2dhg1X4BgHJTBKxw5fZEO+y112qRUxibH93GG/1Bc1cj5sgYAwkKE24MoMexAWHNblkt1WV3byO6jBT626NYREVY6qh/vPRvY8vmRgnFQBq+w7/h51/aKAFPv7cpzS8sXjiBxhCfh4aE8sMadRPadbdkBnf5oleNF43RxDdUNJjefv2MclGHIaWxq5bgje4SzIJ6/09LewYEChzhi1sgSR3hy3+rZ3dIfHT1zyccW3Tqpce65RFXYX2BGUf6OcVCGIedojju8lz4xhfGpgVN59gZxxPiRJY7wJCY6kntXzXK1f78t24fWDJ7bp7tHUXvPVgT0iHAk4FUHJSIbRCRfRApE5Fs9HI8Ukdfs4wdFJN3ef4+IHBWRU/b3u71pp2Fo6a7eC5zRE8BOhzhizczgzxzRHx66cz4hIdaj4sz5Us5dvOZji26dxRmjiQq3fpZrNS2uuUaDf+I1ByUiocALwP3AbOAJEZnt0e3LQLWqZgLPAz+091cAn1DVecAXMeXfA4bmljaOOcJAgeSgiqsaueAQR4yUzBF9kZIYy+rF7mUC72wL3PRHkWGhLJ3qLpey11Hny+B/eHMEtQwoUNULqtoKvAo84tHnEeAle/tNYJ2IiKoeV9USe38OECUikV601TBEHD1ziTZbjjx5fJJrLU0g4Bw9LUofueKInnAu3D144gKl5TU+tGZwOFWZhy9UmdRHfow3HdRE4LKjXWzv67GPqrYDNYDna+ungeOqesNSdhF5WkSOiMiR8vJyz8MGH+BU7wXS6MkSR7jLaqwZYZkj+mLKhGQWzbIqISuwaXvgjqKmjYlhbIL1vtvc1smxIrMmyl/xpoPqKXjvOSPZax8RmYMV9vtqTzdQ1Z+r6hJVXZKaah4ovubG8F7gZI84fKGaJlscMdaII3rkkbvdo6htB/KovB6YSWRFuq9t23vOhPn8FW86qGJgkqOdBpTcrI+IhAEJQJXdTgPeBp5U1fMY/J7juZdpbbMKwqWNTWTSuMAp7b7LiCP6ZO70CUyfMgaAjo5O3v4ocBV9qzKT6foT55XUUV4X2LkGgxVvOqjDwHQRyRCRCGAjsMmjzyYsEQTAY8DHqqoiMhrYDHxbVfd60UbDEOJU760IoPDe5cru4oiVRhzRIyLCZ+9f6mp/sO9MwJbiGB0TwZyJ7uwm+4xYwi/xmoOy55SeBbYCucDrqpojIs+JyMN2txeBZBEpAL4JdEnRnwUygb8VkWz7a4y3bDUMnta2do6cvuhqrwogB/XhabdsenF6ohFH9MLCmWkeo6jjPrbo1nGG+fadqzRrovwQr66DUtUtqpqlqtNU9Qf2vu+o6iZ7u1lVH1fVTFVdpqoX7P1/p6oxqrrQ8VXmTVsNgyM7r5iW1jYAJqQmMHl8Uh9n+AfXG1o5dN4tjlg3x7wH9YbnKOrD/bkBO4paOGU0MZGhAFTWt5JXWudjiwyemEwShiFh5+Gzru2VC6cFzBzOtjNltHdab86ZY2OZNjbWxxb5P8EyigoPDWH5NGcCWRPm8zeMgzIMmorqeg6dLHS1b1+c6UNr+k9zawc7c93iiA3zx/nQmsBBRNj4QHCMom7Pcjuoo4XVNLa0+9AagyfGQRkGzdY9OXTa8fu50ycwZUJghPd2n61w5d0bmxDJgsmBkzPQ1yyYkUZW+ljAGkW99WFgjqImJ48iLcldDv5Ioam2608YB2UYFK1t7Xywz11t1VmewZ9p7+jkg1NXXe17544LmLCkP2DNRS1xtT86EJijKM81UTtyy41Ywo8wDsowKHYfPUd9o7WGJDUxjqVzp/jYov5xtLCa6gZL1BEXFWak5bdAsIyiVmQmER5qvZxcqmzkdHGtjy0ydGEclOGWUVU27zztat+/Zq4r67U/o6psPeWWlt89ZwwRYf5vt78RLKOouKjwbqmtNmeXmlGUn2A+lYZb5sz5Ui6WWEXfIsLDWLdipo8t6h95JXVcqmwEIDxUuGuWkZbfKsEyirpv3jjCQqxRVMG1eiM59xOMgzLcMlt2nnJtr106ndhRgZFw/n3H3NPqGSnERoX50JrAJlhGUUmxEaxyKPo2Z5f60BpDF8ZBGW6J8qo6Djqk5YEijiiuaiTHnmMQgXvmjvWxRYGP5yjq1fcO+9iiW+P+BeMJceTnK7gWeI422DAOynBLbN2T40o7Py9rYsBkjnDOPS1OT2RMfJQPrQkOPNdFbT+YH5BVd1PjIlmR6R5FvXvcM7e1YbgxDsowYFpa2/hwf66rHSijp6r67mmN7ptnRk9DxYIZaSydm+5qv/jfewNSaPDAgvGuLOeni2spLDcl4X2JcVCGAbP7aIFLWj4mKY4lcyb72KL+se3MNTrstEZZ42KZOsakNRpKvvjJlYSGWo+UcxfL2HHobB9n+B/jRkexNMMdDTBzUb7FOCjDgLCk5W5xRKBIy+ub29mV5861dt88k9ZoqBmfmsAnHUUNf/OHAzQ2tfrQolvjwYXjXdvZF69TXNXoQ2tGNv7/ZDH4FTkFJVwqtcJkgSQtf/d4iati7rjRUcw3aY28wqfuWURSQgwANXVNvLH1qI8tGjgTk6JZnD7a1X73uBlF+QrjoAwDYssu98Lcu5bNICba/6Xlpdeb2O5ICvvobRNNWiMvERUZzpMPr3C13915iuJrgZffzjmKOlpUTUl1kw+tGbkYB2XoN2VVdd2ylt+/Zq4Prek/bxws7jb35Hw7Ngw9q2/LZEaGFULt7OzkV2/vCzjBxJSUGOZPskbZqrDlhBlF+QLjoAz95te/P+CSls/PSmPSuESf2tMfcoprOHm5BrDWPW1cOdmMnryMiPCVx1bT9Vs+nnuZo2cu+dSmW+GhRe5R1KHzVZTVNvvQmpGJVx2UiGwQkXwRKRCRb/VwPFJEXrOPHxSRdHt/sohsF5F6EfmxN2009I8T+cXszz7vaj9232IfWtM/OjqV1w5edrVvn57C5ORRPrRo5JCRlsL6VbNc7V++tZe2tg4fWjRwpo6JZfbEeAA6FV4/WBxwI8FAx2sOSkRCgReA+4HZwBMiMtuj25eBalXNBJ4Hfmjvbwb+FvgLb9ln6D/t7R381xu7Xe07bpvOnMwJPrSof+zKK6ek2nrrjQwP4ZO3+b/NwcTnHlzGqKgIAK5W1PKHHSd9bNHAeXix+38m++L1buvoDN7HmyOoZUCBql5Q1VbgVeARjz6PAC/Z228C60REVLVBVfdgOSqDj9m0/SQl5VaYLCoynCcfWdHHGb6nsaWd3x9zZwJ4cMF4RsdE+NCikUd8bDRPPOjOMPHmB8coqwqsJKyZY2O5c5Y70/nv9l+iprHNhxaNLLzpoCYClx3tYntfj31UtR2oAfpdmEdEnhaRIyJypLy8vO8TDAOmorq+m1R44/1LXTJif+bd7FLqm63y3cmxEaw3Ofd8wn23z2GSnQarpbWN51/6iPb2wAr1PbY0jeRY6+WmoaWD3+y9aEJ9w4Q3HVRPM9Gef9X+9LkpqvpzVV2iqktSU1P7PsEwYH759j5a26wH/eTxSTwQAMq9azXNfJxT5mo/tizN1HvyEaGhIfyPz64hxBamnC26xqtbAiuZbFREKH98R7qrnX3xOocvBJ50PhDx5qe2GJjkaKcBntkXXX1EJAxIAEyQ10/IzrvMgRMXXO2vPH6HK5WNP/PGoWLabVl55thYlmT4v9owmJmRMY4nHlzmar+9LZvjuZd7OcP/mDUxnrWOUN9v912ktsmE+ryNN582h4HpIpIhIhHARmCTR59NwBft7ceAj9WMnf2CtrYOXnxzj6u9dmkWs6eN7+UM/yC3pJbsi9dd7c+umGRk5X7Ao+sXsnCm+331P17+mEdPwj4AABV3SURBVKqawErE+vjSNJJMqG9Y8ZqDsueUngW2ArnA66qaIyLPicjDdrcXgWQRKQC+Cbik6CJSBPwb8MciUtyDAtDgRTbtOOESRkRHRQSEMKK5tYOX9150tVdOTyYj1f/ny0YCIsLXP383ifGWzL+2vol///U2Ojs7fWxZ//EM9R0vus7hQhPq8yZejdeo6hZVzVLVaar6A3vfd1R1k73drKqPq2qmqi5T1QuOc9NVNUlVY1U1TVXPeNNWg5uyqjreeN8tjHjigaWMjvPv9UOqyi92FXKtxsqyHhEWwqeWeGpyDL4kIS6ab3xhnWviOaeghDe2HvOpTQNl9sR41sxMcbV/u9eE+ryJ/08oGIYVVbUWVdpKqykTktmweo6Preqb909e5ViRO7T35OopJBpZud8xL2sij224zdV+4/0jnD53xYcWDZzHl03qFup72YT6vIZxUIZuvLH1KIdOFbnaTweAMCL3Si1vHXE/5NbNGdOtMqrBv/jMfbe5Fnor8O+/3kZNXeAkY432CPUdK7rOu6ZulFfw7yePYVj5cN8ZXnvviKt9z6pZzJzq33WTKutb+Nn2C3S9wGaOjeXxZWm+NcrQKyEhIfz5k+uIi4kCoLq2kX//9baASoXkGer7/dEStp686kOLghPjoAwAHDpVxM9e2+VqL5iRxlOfXu1Di/qmtb2Tn2w771qQmzAqnGfunkqYn4/4DJCUEMPXP3+3q33ybDE/fPF915q7QGDjismuXH1gLW/YlnPNhxYFH+aTbCDvwlX+7VcfulZIT52Uyv/+0r2EhYX61K6+eHX/JYrKrWqnoSHCM3dPNemMAojFsyfzmQ1LXO3juZf5h5+/T0trYIgOIsJC+NN7ppE1Lta175X9l9mdb7LaDBXGQY1wLpVW8YOfbXGJIsalxPM3X32A6Cj/ftDvzi9nV767hPtnlqcxfVycDy0y3Aqf2XAbj93nFk2cPFvMD372Hs0tgeGkIsNC+fq905k2xr2c4dd7LrL/XKUPrQoejIMawVRU1/N3P91MY3MrYCX3/JtnHiQhLtrHlvVOfmkdv93rri+0fFoSd88e40OLDLeKiPDEA0vZ+IA7qWxOQQnP/WQzjU2tPrSs/0RFhPKN+6a7Srmowi92FXKk0CTFGSzGQY1Q6hqa+f5PNlN53VrNHxkRzt8+8wDjUxN8bFnv7D1bwfPvnXWlMkpLiubJ1VNMtogA5/H7buPzn1juaucXXuW5n7xLQ1OLD63qP6Miw/jm/VmkJVkvd6rw/7YXcqzILOQdDMZBjUDOXyrn//v3dyi+Zn14QkND+Kun7mPqJP9NuNvZqbx5qJhf7ipyOae4qDC+tn4akeH+PVdm6B+Prl/EH39ylat97mIZ333hXeoaAqPqTmyU5aTGjbbUiR2dyv/96Dwv771ISwApFP0JCZYFZkuWLNEjR4703XEE09HRydvbsnntvSPdUsz8zyfXs/q2TB9a1jvNrR38187Cbjn2JiZG8+y9maTGRfrQMoM3eH93Dv/vTXeBzMT4UTz9mTUsm5fuO6MGQHVDK/+8OZ+yWvfoLzUukj9Zm06WmScFQESOquqSPvsZBzUyKC2v4T9/u538QvdajciIcL76mTtYuzTLh5b1TkVdCz/+sIDiKvdCzvmTEvjKXVOJjjAjp2Dlo/25/PTVnd1q76xcOI2nHrvd79NuAdQ2tfHrPRe7vVSJwPo5Y3l0ycQRX/7FOCgDYKUu+vhgHi/+975u8t3pU8bwjS+s8+s5p4Jr9bzwYQF1ze61MffOG8tjS9MICTFzTsHOoVNF/PS1nd2yTMRER/KlT61i7dIsv593VFX2navktQOXaWx1h/jGJkTy5bUZTB0T28vZwY1xUAYullTxyuZDHD5d5NoXEhLCZzbcxqfWL/LbFEbXaprZnF3KgYJK7OkmwkKEz6+ewuqslN5PNgQVdQ3N/Oqd/ew4lN9t/4IZaTyzcS1jkvw/ZFZV38pLe4rIKa517ROBpRlJ3D1nDNPGxPi9sx1qjIMaobS3d3DgRCFb9+Zw5nz3/GATUhP4xhfWkTnFPyXZpdeb2JxdysHzVTj/LWNtMYSJ349csvMu89NXd1FeXefaFx4WyurbMlm/YhYzMsb69UNeVdmdX8FrBy/T0ta9xMjk5FHcOSuV5dOSRozgxzioEUZ5VR0f7svlw/251NbfmHhzw+o5PPnICiIjwn1gXe9cqW5i8/FSDhd2d0xg5Tz7wuopRgxhoLmljVc2H2bzzpN4PrXSxiayfuUs1i6dTnys/67jq6hr4dd7LnLmSu0Nx0ZFhHJ7VgprZqYwLiHKrx3uYDEOKshpbmnj3MUy8gqvcqaglFNni2/40IaEhLBsXjqfuHO+XyV97exUiioayC2pI7eklvzSuhsc05y0eD6xaAKZY0dunN7QM2eLrvGz13dTdKXihmOhoSEsm5fB6sXTyEofS1KCfxasvFjRwPYz5Rw8X0lbx43P4MSYcLLGxVlf42ODzmEZBxVENLe0UV5dz6XSKvILr5J34SqFxRV03uRvlxg/intvn836lbP84gPa3tFJWW0LuSW15JXUkV9a123S2Mm8SQl8YtH4ET2BbOgbVeXcxTI+2p/LnmPnb5q/LyUxlulTxjJ9yhiypoxh2uRUIsLDhtnam9PQ0s7esxXsyC3vJkv3JD46jKxxcUxOGcWY+EjGxEeRGhcZsEpWv3BQIrIB+BEQCvyXqv6jx/FI4NfAbUAl8FlVLbKPfRv4MtABfF1Vt/Z2r0ByUJ2dnTS1tNHQ1EpjUwsNTa2u7bqGFiqq66moruNaVR3lVXXUN/ZvNf38rDTuWz2bpXPTvS6AUFXaOpTmtg6aWu2vtg6q6luprG+loq7l/2/v3GPkqu47/vnOY2ef9vqZssaAQyE1UYtjCCWClIdo01LVLknTGkVqolRCpKHEVGlFRJUiolaUPiIioqS0oNKEQNIEtU4rEgh1QqqAbUB+QGyMCXawAdvCNgZnvY+5v/5xznjvjnd217s7c6/t30ca3XPPPffOd87cmd89v3vu7xde7wxy8MjgcSOk47Qvns3vLe/zFO3OCdN/dJCfbHyZx3+ylZd27Ru3bUFiXm83c3u7mNfbxfzebubO7grrs7voaC/TXinTUSnTUWmjVCq0ZORiZryw5zA/2rafrXsOc7TuPlUjuttLLOipsKCnwuzOMp2VIt2VEl2VEt3tJboqRTrbSrSVCrSVCpSLykW0/8wNlKQisB34TWA3sAG4Pp26XdKfAr9mZjdKWgVcZ2Z/JOkC4CHgEqAP+AFwvpk1fBx7Ogbqbx9ax+Zdx9//IDrNavVWW49Ty4xwYpnVlqGcpMrVJKFaTUiShGpiVBObseybvT0dLJjTzYK5PSyY20NX5xj3aWzUIn6e0Z8rqX2GVDmxoHW4aiRmDFUTkgSGk4TB4YSBoeRYRIepMKerzK/0zWJpXw9L+2Z59ltnRtj12gF+/Mx2tr7yBi//fP+xIMhTpVAo0FEpU2krUSoWKBULFIsFisXiSLkgCgUhCVErQ0HBENTsW83Q1a/Xk5jx9iAcHIADA8aBozA0jd9aPUEbFCUKCuGEJBChvlZWWmt8EbcB/PNNVzGnp32KGiZnoJo51r0E2GFmP4uCHgZWAj9NtVkJ3B7L3wbuUfjWVgIPm9kA8IqkHfF4TzVD6NY9h9i29xfNOHSkwKioUid4QSZBW7lEpVyiq6ON7s4KXZ3tx66EDg7DwX39QH6zkvZ2llmyoIuli4JROtV86k4+OLtvLmevuBQIM1pffeMg23fuZfuufby0cy979h2a4AijSZKEI/0DmcYE7AYGKXKUMkMqMkSRYYoMqYid6J/JDDJd4z8ZmmmgFgGvptZ3A7/eqI2ZDUt6C5gX65+u23dR/RtIugG4AeCss86astAsvuJiQRQLtauxAsXCyNVZuVSk0lairVykrRyW2agcn1JRdJSLdLQVaS8XaS8XmN1ZZn5PhfndFeb1tDG/u8Lc7rbT/sl5p/WUSkWWnDmfJWfO50OXvxeAgcEh3jx0hDcPHeHAW0di+R3ePHSEQ2//gqMDQ/QPDNF/NCzTIcGyQkCFKhWqo1whZlClEA1WgSoFqiqQIKqEZUKBKsIQJgWvz0z9l7TgArOZBmos9fXj1EZtJrMvZnYvcC8EF9+JCqzxyWuWsmv/4SioNgwfLUExckHtb1bSSJ3CsP64ZTRA5Wh4SqXgFigUClP+bjWFkys9TK9pT28LQ3pRLOjYML4gUYqui3IxfKZSUZQKhWCY2oqUc+DLdpwTodJWpm9hL30Leydsa2YMDyf0DwwyMDjMcDU55rIfHg7l4ei+T7v2k+jGr7nL08erd6+fCHb8X+CUqCbh/nE1MYYTI4nak3iromphpq1FnYalbgfUlsbszua75ZtpoHYDi1PrZwKvNWizW1IJmA0cmOS+M8aVy6Y++nIc59REEuVykXI5v89Vneo08xJ4A3CepCWS2oBVwJq6NmuAj8fyHwD/a+ESYw2wSlJF0hLgPGB9E7U6juM4OaNpI6h4T+km4PuEaeb3m9kLku4AnjGzNcB9wNfiJIgDBCNGbPctwoSKYeDT483gcxzHcU49/EFdx3Ecp6VMdpq53+V2HMdxcokbKMdxHCeXuIFyHMdxcokbKMdxHCeXnDKTJCTtB3ZN8zDzgeNj+OeLk0EjnBw6XePMcTLodI0zx3R1nm1mCyZqdMoYqJlA0jOTmVmSJSeDRjg5dLrGmeNk0OkaZ45W6XQXn+M4jpNL3EA5juM4ucQN1GjuzVrAJDgZNMLJodM1zhwng07XOHO0RKffg3Icx3FyiY+gHMdxnFziBspxHMfJJaeVgZJ0v6R9kp5P1X1B0mZJGyU9Jqkv1kvSlyTtiNuXZ6Uxte2zkkzS/LxplHS7pD2xHzdKuja17XNR44uSPpSVxlj/Z1HHC5LuylJjI52Svpnqx52SNmaps4HGZZKejhqfkXRJrM/knBxH54WSnpK0RdJ3Jc1KbcuiLxdLWitpazwHPxPr50p6XNJLcTkn1re8P8fR+NG4nki6uG6f5vRlLcvj6fACfgNYDjyfqpuVKt8MfDWWrwUeJSSivRRYl5XGWL+YkLpkFzA/bxqB24HPjtH2AmATUAGWAC8DxYw0XgX8AKjE9YVZahzv+05t/0fg8znsy8eA30mdhz/M8pwcR+cG4IpY/iTwhYz78gxgeSz3ANujlruAW2P9rcDfZdWf42hcCrwH+CFwcap90/rytBpBmdmThLxT6brDqdUuRlLLrwT+3QJPA72SzshCY+SLwF+m9OVR41isBB42swEzewXYAVzSNHGRBho/BdxpZgOxzb4sNY6jEwhXz8AfAg9lqbOBRgNqo5HZjGS8zuScHEfne4AnY/lx4CMpnVn05etm9lwsvw1sBRZFPQ/EZg8Av5/S2dL+bKTRzLaa2Ytj7NK0vjytDFQjJP2NpFeBjwGfj9WLgFdTzXbHupYjaQWwx8w21W3KjcbITdENcX/NRUG+NJ4PfFDSOkk/kvT+WJ8njWk+COw1s5fiep50rgb+Pv5u/gH4XKzPk0aA54EVsfxRgicCcqBT0jnA+4B1wLvM7HUIBgJYGJtlqrNOYyOaptENFGBmt5nZYuBB4KZYrbGatk5VFCF1ArcxYjhHbR6jLqvnBr4CnAssA14nuKYgXxpLwByCq+QvgG/FUUqeNKa5npHRE+RL56eAW+Lv5hZCdmzIl0YIbr1PS3qW4K4ajPWZ6pTUDXwHWF3nxTmu6Rh1LdGZB41uoEbzDUZcALsZudoCOJMRN0YrOZfg190kaWfU8ZykXyI/GjGzvWZWNbME+BdGhvi50Ri1PBLdJeuBhBD0Mk8aAZBUAj4MfDNVnSedHwceieX/IJ/fN2a2zcx+y8wuIhj7l+OmzHRKKhP++B80s1of7q257uKy5n7ORGcDjY1omsbT3kBJOi+1ugLYFstrgD+Os2guBd6qDcFbiZltMbOFZnaOmZ1DOBmWm9kbedEIx35UNa4juFYgaFwlqSJpCXAesL7V+iL/CVwNIOl8oI0QkTlPGmtcA2wzs92pujzpfA24IpavBmpuyNyckwCSFsZlAfgr4KtxUyZ9GUfs9wFbzeyfUpvWEIw+cflfqfqW9uc4GhvRvL5s9oyQPL0IV1CvA0OEP/o/IVwlPA9sBr5LuBkIYdj6ZcIV1xZSs1ZarbFu+05GZvHlRiPwtahhczxhz0i1vy1qfJE48ysjjW3A1+P3/RxwdZYax/u+gX8DbhyjfV768nLgWcLsrXXARVmek+Po/AxhFtp24E5i9JwM+/JygvtrM7Axvq4F5gFPEAz9E8DcrPpzHI3XxX4dAPYC3292X3qoI8dxHCeXnPYuPsdxHCefuIFyHMdxcokbKMdxHCeXuIFyHMdxcokbKMdxHCeXuIFynBNA0jt165+QdM8E+6yQdOsEba6U9N8Ntq2OEUUa7fttSe8e7/iTQdLDdc8FOk6muIFynCZjZmvM7M5pHGI1MKaBkvReQuTon03j+DW+QghI7Di5wA2U48wQkhZI+o6kDfF1Waw/NsqSdK5CHqUNku6oG5F1x9HQNkkPxugBNwN9wFpJa8d4248xEnUASb8t6TlJmyQ9Eetul/SAQr6znZI+LOkuhRxJ34thbQB+DFwTwyw5Tua4gXKcE6NDI8kENwJ3pLbdDXzRzN5PiOn4r2Psfzdwd2xTH6/sfYTR0gXAu4HLzOxLsd1VZnbVGMe7jBDRAUkLCHEQP2JmFxKid9c4F/hdQmqErwNrzexXgf5Yj4U4ijuACyfVE47TZPxKyXFOjH4zW1ZbkfQJoJZd9BrgghDKDIBZknrq9v8AI7l+vkFIVVFjvcXYe9H4nQP83wR6zgD2x/KlwJMWcvJgZuncSI+a2ZCkLUAR+F6s3xLfp8Y+wojt2Qne13Gajhsox5k5CsAHzKw/XZkyWBMxkCpXmdzvsx9or70VjdMc1JI0JpKGbCTGWVL3Pu3xmI6TOe7ic5yZ4zFG8okhadkYbZ5mJKXLqkke921CLqOx2Ar8ciw/BVwRI0ojae4kj5/mfOCFKeznODOOGyjHmTluBi6OWYV/Ctw4RpvVwJ9LWk9wz701iePeCzzaYJLE/wBXApjZfuAG4BFJmxidS2pCJL2L4MLMLD2G46TxaOaO00Li80z9ZmaSVgHXm9nKaRyvA1hLmFBRnaa2W4DDZnbfhI0dpwX4PSjHaS0XAffEpHCHCCnJp4yZ9Uv6a2AR8PNpajtEyOvlOLnAR1CO4zhOLvF7UI7jOE4ucQPlOI7j5BI3UI7jOE4ucQPlOI7j5BI3UI7jOE4u+X9eYhzt+SiSYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "posterior_a = suite.Marginal(0)\n",
    "posterior_b = suite.Marginal(1)\n",
    "\n",
    "thinkplot.Pdf(posterior_a, label='A')\n",
    "thinkplot.Pdf(posterior_b, label='B')\n",
    "thinkplot.decorate(xlabel='Height (cm)',\n",
    "                   ylabel='PMF',\n",
    "                   title='Posterior marginal distributions')\n",
    "\n",
    "posterior_a.Mean(), posterior_b.Mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second tallest problem\n",
    "\n",
    "In a room of 10 randomly chosen U.S. residents, A is the second tallest.  How tall is A?  What is the probability that A is male?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# The prior for A and B is the mixture we computed above.\n",
    "\n",
    "A = mix\n",
    "B = mix;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def faceoff(player1, player2, data):\n",
    "    \"\"\"Compute the posterior distributions for both players.\n",
    "    \n",
    "    player1: Pmf\n",
    "    player2: Pmf\n",
    "    data: margin by which player1 beats player2\n",
    "    \"\"\"\n",
    "    joint = make_prior(player1, player2)\n",
    "    joint.Update(data)\n",
    "    return joint.Marginal(0), joint.Marginal(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# We can think of the scenario as a sequence of \"faceoffs\"\n",
    "# where A wins 8 and loses 1\n",
    "\n",
    "for i in range(8):\n",
    "    A, _ = faceoff(A, B, 'A')\n",
    "    \n",
    "A, B = faceoff(A, B, 'B');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181.60660153115964"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# Here's the posterior distribution for A\n",
    "\n",
    "thinkplot.Pdf(A)\n",
    "A.Mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9167980720726623"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# Now we can compute the total probability of being male,\n",
    "# conditioned on the posterior distribution of height.\n",
    "\n",
    "total = 0\n",
    "for h, p in A.Items():\n",
    "    total += p * prob_male(h)\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# Here's a second solution based on an \"annotated\" mix that keeps\n",
    "# track of M and F\n",
    "\n",
    "annotated_mix = Suite()\n",
    "for h, p in male_height_pmf.Items():\n",
    "    annotated_mix['M', h] = p * 0.49\n",
    "    \n",
    "for h, p in female_height_pmf.Items():\n",
    "    annotated_mix['F', h] = p * 0.51\n",
    "    \n",
    "annotated_mix.Total()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# Here's an updated Heights class that can handle the\n",
    "# annotated mix\n",
    "\n",
    "class Heights2(Suite, Joint):\n",
    "    \n",
    "    def Likelihood(self, data, hypo):\n",
    "        \"\"\"\n",
    "        \n",
    "        data: who is taller, A or B\n",
    "        hypo: (MF1, h1), (MF2, h2)\n",
    "        \"\"\"\n",
    "        (_, h1), (_, h2) = hypo\n",
    "        if data == 'A':\n",
    "            return 1 if h1 > h2 else 0\n",
    "        if data == 'B':\n",
    "            return 1 if h2 > h1 else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "# Everything else is pretty much the same\n",
    "\n",
    "from thinkbayes2 import MakeJoint\n",
    "\n",
    "def faceoff(player1, player2, data):\n",
    "    joint = Heights2(MakeJoint(player1, player2))\n",
    "    joint.Update(data)\n",
    "    return joint.Marginal(0), joint.Marginal(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "A = annotated_mix\n",
    "B = annotated_mix;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "for i in range(8):\n",
    "    A, _ = faceoff(A, B, 'A')\n",
    "    \n",
    "A, _ = faceoff(A, B, 'B');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pmf({'M': 0.9167985976340078, 'F': 0.0832014023659922})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# Now the posterior distribution for A contains the\n",
    "# probability of being male\n",
    "\n",
    "A_male = Joint(A).Marginal(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181.60660153115973"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "# The posterior distribution for A also contains the\n",
    "# posterior probability of height\n",
    "\n",
    "A_height = Joint(A).Marginal(1)\n",
    "\n",
    "thinkplot.Pdf(A_height)\n",
    "A_height.Mean()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}