{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Think Bayes\n", "\n", "This notebook presents example code and exercise solutions for Think Bayes.\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 classes from thinkbayes2\n", "from thinkbayes2 import Hist, Pmf, Suite, Beta\n", "import thinkplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unreliable observation\n", "\n", "Suppose that instead of observing coin tosses directly, you measure the outcome using an instrument that is not always correct. Specifically, suppose there is a probability `y` that an actual heads is reported as tails, or actual tails reported as heads.\n", "\n", "Write a class that estimates the bias of a coin given a series of outcomes and the value of `y`.\n", "\n", "How does the spread of the posterior distribution depend on `y`?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "\n", "# Here's a class that models an unreliable coin\n", "\n", "class UnreliableCoin(Suite):\n", " \n", " def __init__(self, prior, y):\n", " \"\"\"\n", " prior: seq or map\n", " y: probability of accurate measurement\n", " \"\"\"\n", " super().__init__(prior)\n", " self.y = y\n", " \n", " def Likelihood(self, data, hypo):\n", " \"\"\"\n", " data: outcome of unreliable measurement, either 'H' or 'T'\n", " hypo: probability of heads, 0-100\n", " \"\"\"\n", " x = hypo / 100\n", " y = self.y\n", " if data == 'H':\n", " return x*y + (1-x)*(1-y)\n", " else:\n", " return x*(1-y) + (1-x)*y" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ds1X1TFW9CuwDds6r2Qnc0R7fC1ycJK19X1W9UlVfAWZbf+cDD1fVy1V1FPgc8J45/d0MfAioBc5LkrRIJgmKc4Bn52wfam0ja9oT/0vA+s6xTwAXJVmf5HTgXcAmgCTvBr5eVY+d8GwkSYtu7QQ1GdE2/5X+8WpGtlfVU0luAh4AvgM8BhxtofFh4B1jB5XsBnYDnHvuuePKJUkLNMkVxSHaq/1mI/Dc8WqSrAXOBF7sHVtVe6rqgqq6qNU+Dfw0sAV4LMlXW/2XkvzE/EFV1e1VNaiqwYYNGyaYhiRpISYJikeBrUm2JFnH8M3p6Xk108Cu9vhy4MGqqtY+1T4VtQXYCnwRIMnZ7fe5wHuBu6rq8ao6u6o2V9VmhkFzQVV946RmKUlasLG3nqrqaJKrgfuBNcDeqjqQ5EZgpqqmgT3AnUlmGV4dTLVjDyS5B3gSOAp8oKp+0Lq+L8l64Put/chiT06SdPIyfOG/vA0Gg5qZmVnqYUjSspJkf1UNxtX5zWxJUpdBIUnqMigkSV0GhSSpa0W8mZ3kMPAnCzz8LOBbizic5cA5rw7OeXU4mTn/raoa+0W0FREUJyPJzCTv+q8kznl1cM6rw1/GnL31JEnqMigkSV0GBdy+1ANYAs55dXDOq8Mpn/Oqf49CktTnFYUkqWtVB8W4tcCXuySbkjyU5Km2Nvk1rf31SR5I8nT7/bqlHutia0vufjnJf2vbW9p67k+39d3XLfUYF1OS1ya5N8kft/P991b6eU7ywfbf9RNJ7kryN1baeU6yN8kLSZ6Y0zbyvGboY+357I+SXLBY41i1QTHhWuDL3VHg31bV+cCFwAfaHK8HPltVW4HPtu2V5hrgqTnbNwE3tzkfYbjO+0ryUeAzVfV3GK5D/xQr+DwnOQf4N8Cgqt7M8C9bT7HyzvPHgR3z2o53Xi9huJTDVoaLut22WINYtUHBZGuBL2tV9XxVfak9/jbDJ49z+PNrnN8BXLY0Izw1kmwE/hnwu207wNsYrucOK2zOSc4ALmL45/6pqler6k9Z4eeZ4TIJr2mLpZ0OPM8KO89V9b8YLt0w1/HO607gEzX0MPDaJG9YjHGs5qCYZC3wFSPJZuDngEeAH6+q52EYJsDZSzeyU+K3gQ8Bf9a21wN/2tZzh5V3rn8KOAz813a77XeT/Bgr+DxX1deB3wK+xjAgXgL2s7LP8zHHO6+n7DltNQfFJGuBrwhJ/iZwH3BtVf2/pR7PqZTkUuCFqto/t3lE6Uo612uBC4DbqurngO+ygm4zjdLuy+9kuHTyTwI/xvDWy3wr6TyPc8r+O1/NQTHJWuDLXpK/zjAkfq+qPtmav3nskrT9fmGpxncK/APg3W3N9X0Mb0X8NsPL8GMrOq60c30IOFRVj7TtexkGx0o+z/8U+EpVHa6q7wOfBP4+K/s8H3O883rKntNWc1BMshb4stbuze8Bnqqq/zxn19w1zncBn/rLHtupUlU3VNXGtub6FMP1238JeIjheu6w8ub8DeDZJH+7NV3McPnhFXueGd5yujDJ6e2/82NzXrHneY7jnddp4H3t008XAi8du0V1slb1F+6SvIvhq81ja4H/pyUe0qJK8g+B/w08zo/u1/8Hhu9T3AOcy/B/uF+oqvlvmC17SX4euK6qLk3yUwyvMF4PfBn4F1X1ylKObzEleQvDN+/XAc8Av8zwheCKPc9Jfg34RYaf7vsy8K8Y3pNfMec5yV3AzzP8C7HfBP4j8IeMOK8tMG9h+Cmpl4FfrqpFWSN6VQeFJGm81XzrSZI0AYNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1/X+sh+5kGDOUsQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", "\n", "# Now let's initialize an UnreliableCoin with `y=0.9`:\n", "\n", "prior = range(0, 101)\n", "suite = UnreliableCoin(prior, y=0.9)\n", "thinkplot.Pdf(suite)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", "\n", "# And update with 3 heads and 7 tails.\n", "\n", "for outcome in 'HHHTTTTTTT':\n", " suite.Update(outcome)\n", " \n", "thinkplot.Pdf(suite)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "\n", "# Now let's try it out with different values of `y`:\n", "\n", "def plot_posterior(y, data):\n", " prior = range(0, 101)\n", " suite = UnreliableCoin(prior, y=y)\n", " for outcome in data:\n", " suite.Update(outcome)\n", " \n", " thinkplot.Pdf(suite, label='y=%g' % y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", "\n", "# The posterior distribution gets wider as the measurement gets less reliable.\n", "\n", "data = 'HHHTTTTTTT'\n", "plot_posterior(1, data)\n", "plot_posterior(0.8, data)\n", "plot_posterior(0.6, data)\n", "thinkplot.decorate(xlabel='Probability of heads (x)',\n", " ylabel='PMF')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", "\n", "# At `y=0.5`, the measurement provides no information, so the posterior equals the prior:\n", "\n", "plot_posterior(0.5, data)\n", "thinkplot.decorate(xlabel='Probability of heads (x)',\n", " ylabel='PMF')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", "\n", "# As the coin gets less reliable (below `y=0.5`) the distribution gets narrower again. \n", "# In fact, a measurement with `y=0` is just as good as one with `y=1`, \n", "# provided that we know what `y` is.\n", "\n", "plot_posterior(0.4, data)\n", "plot_posterior(0.2, data)\n", "plot_posterior(0.0, data)\n", "thinkplot.decorate(xlabel='Probability of heads (x)',\n", " ylabel='PMF')" ] }, { "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 }