{ "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 evaluators\n", "\n", "This exercise is inspired by a question posted by a “redditor” named dominosci on Reddit’s statistics “subreddit” at http://reddit.com/r/statistics.\n", "\n", "Reddit is an online forum with many interest groups called subreddits. Users, called redditors, post links to online content and other web pages. Other redditors vote on the links, giving an “upvote” to high-quality links and a “downvote” to links that are bad or irrelevant.\n", "\n", "A problem, identified by dominosci, is that some redditors are more reliable than others, and Reddit does not take this into account.\n", "\n", "The challenge is to devise a system so that when a redditor casts a vote, the estimated quality of the link is updated in accordance with the reliability of the redditor, and the estimated reliability of the redditor is updated in accordance with the quality of the link.\n", "\n", "One approach is to model the quality of the link as the probability of garnering an upvote, and to model the reliability of the redditor as the probability of correctly giving an upvote to a high-quality item.\n", "\n", "Write class definitions for redditors and links and an update function that updates both objects whenever a redditor casts a vote." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "\n", "# Here's one possible model:\n", "\n", "# Each article has a quality Q, which is the probability of \n", "# eliciting an upvote from a completely reliable redditor. \n", "\n", "# Each user has a reliability R, which is the probability of\n", "# giving an upvote to an item with Q=1.\n", "\n", "# The probability that a redditor with reliability R gives an\n", "# upvote to an item with quality Q is `R*Q + (1-R) * (1-Q)`\n", "\n", "# Now when a redditor votes on a item, we simultaneously update our\n", "# belief about the redditor and the item." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", " \n", "# Suppose we start with a redditor who has demonstrated some reliability.\n", "\n", "beta = Beta(2, 1)\n", "redditor = beta.MakePmf(11)\n", "thinkplot.Pdf(redditor)\n", "thinkplot.decorate(xlabel='Reliability (R)',\n", " ylabel='PMF')\n", "\n", "mean_r = redditor.Mean()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", " \n", "# And a completely unknown item.\n", "\n", "beta = Beta(1, 1)\n", "item = beta.MakePmf(11)\n", "thinkplot.Pdf(item)\n", "thinkplot.decorate(xlabel='Quality (Q))',\n", " ylabel='PMF')\n", "\n", "mean_q = item.Mean()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "\n", "class Pair(Suite):\n", " \"\"\"Represents hypotheses about the reliability and quality.\"\"\"\n", "\n", " def Likelihood(self, data, hypo):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " hypo: q, r\n", " data: 'up' or 'down'\n", " \"\"\"\n", " q, r = hypo\n", " if data == 'up':\n", " return r * q + (1-r) * (1-q)\n", " elif data == 'down':\n", " return r * (1-q) + (1-r) * q\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Solution\n", " \n", "# And here are the results. Since we knew nothing about the item,\n", "# the vote provides no information about the redditor:\n", "\n", "d={}\n", "for r, p1 in redditor.Items():\n", " for q, p2 in item.Items():\n", " d[q, r] = p1 * p2\n", " \n", "suite = Pair(d);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5000000000000003" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", " \n", "suite.Update('up')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", " \n", "redditor_post = Pmf()\n", "for (q, r), p in suite.Items():\n", " redditor_post[r] += p\n", " \n", "redditor_post.Total()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XuYVfV97/H3xwHUxPstUZBAKrbeUiI7ENOIl4jhpDEYg4CKwdYWTavJaRtPzHPaVGnaY3La5nghJiTeb0Qx6iTGEBNATQrKoHjB1IAoOmClgqJBRGf4nj/WmmEz7D2z57L29fN6Hh73XmvtNb+9HpwPa+21fx9FBGZmZtVml0oPwMzMrBAHlJmZVSUHlJmZVSUHlJmZVSUHlJmZVSUHlJmZVSUHlJmZVSUHlJmZVSUHlJmZVaVBlR5AORxwwAExYsSISg/DzMyAZcuWvRYRB/a0XUME1IgRI2hpaan0MMzMDJC0ppTtfInPzMyqkgPKzMyqkgPKzMyqkgPKzMyqkgPKzMyqkgPKzMyqkgPKzMx6pa2tvSw/xwFlZmYl2bxlK1fduoB/veFBIiLzn9cQX9Q1M7P+Wf6fLzP79kVs3LQZgEWP/Y6Txv1hpj/TAWVmZkW9s/U9brpvMb/4zbM7LF+5Zr0DyszMKuPZ51/hmtsW8uqGNzuX7fn+3bhgyniOG/3hzH++A8rMzHbw7ntt3HH/Un6y8EnyP2kae8wILpx6AnvvuXtZxuGAMjOzTqvWrOfq2xbS+urrncvet9sQ/mLyJxmfG4Wkso3FAWVmZrS1tXPXLx7nx794nG15d+iN/qND+dK0Ezhg3z3KPqZMbzOXNFHSc5JWSbq0wPrxkh6X1CZpct7ykyQtz/vzjqTT03U3Snohb93oLN+DmVm9W7NuA1/793uYN39ZZzjtOmQwF0wZz99f+JmKhBNkeAYlqQmYDUwAWoGlkpojIv9WkJeA84Cv5r82IhYCo9P97AesAn6Rt8klETEvq7GbmTWC9vZt3LfgSeY+sJT29m2dy4/8g4P567NP4oMH7FXB0WV7iW8ssCoiVgNImgtMAjoDKiJeTNdtK7SD1GTggYh4O7uhmpk1lrXr3+DqWxewcs36zmWDBzVxzmfH8dkTjynrZ03FZBlQQ4GX8563AuP6sJ9pwL93WfbPkr4B/Aq4NCK2dn2RpJnATIDhw4f34ceamdWfiOBnDz/DLc1LeC9vyqLDhh/ExdNPYtgH9q3g6HaUZUAVit9ezY0h6WDgGGB+3uKvA/8FDAHmAF8DZu30gyLmpOvJ5XLZz8lhZlbl1m98i9m3L+SZles6lzU17cKUiTk+/6nRNDVV1+x3WQZUK3Bo3vNhwLoi2xYzBbgnIt7rWBARr6QPt0q6gS6fX5mZ2Y4iggWP/ifX//g/eGdr569Thh+8H18592RGDD2ggqMrLsuAWgqMkjQSWEtyqe7sXu7jLJIzpk6SDo6IV5RcID0deGYgBmtmVo82btrMtXMf4vFnX+pcJuCMCcdy5qfHMHhwU+UG14PMAioi2iRdRHJ5rgm4PiJWSJoFtEREs6SPAfcA+wKnSbo8Io4CkDSC5AzsoS67vk3SgSTHeDlwYVbvwcysVkUEv3n8eebc9Qibt2z/mP6QA/fm4uknc/iID1RwdKVROaZMr7RcLhctLS2VHoaZWVlsemsLc+56hCVPrt5h+WdP+Ahnf/Zj7DpkcIVGlpC0LCJyPW3nmSTMzOrIY0+/yLVzH+LN32/pXHbgvnty0TkncvSooRUcWe85oMzM6sDmLVu57u7f8NDS3+2wfMInjmDGpOPYfbchFRpZ3zmgzMxqXNcyQYB993off3XWiRx7ZO1+D9QBZWZWo4qVCY7PjeL8L3ySPd63a4VGNjAcUGZmNahQmeBee+zOzDOPL0uZYDk4oMzMakixMsFxHxnJBVPGl61MsBwcUGZmNaKaygTLwQFlZlblqrFMsBwcUGZmVWzNug1cdetCXlz7WueyXYcM5rzTj2PCJ46ou7OmfA4oM7MqtG3bNu79VfWWCZaDA8rMrMrUQplgOTigzMyqRC2VCZaDA8rMrArUWplgOTigzMwqqFbLBMvBAWVmViG1XCZYDg4oM7Myq4cywXJwQJmZlVG1lwlWEweUmVmZ1FOZYDk4oMzMMlasTPCU447gvNNrs0ywHBxQZmYZqtcywXJwQJmZZaBYmeDxY0bxF5Nrv0ywHDINKEkTgSuBJuCHEXFFl/Xjgf8HfASYFhHz8ta1A0+nT1+KiM+ly0cCc4H9gMeBcyPi3Szfh5lZbxQqE9zz/btxwZTxdVMmWA6ZBZSkJmA2MAFoBZZKao6I/H9OvAScB3y1wC62RMToAsu/BXwnIuZK+h5wPnDtgA7ezKwPipUJjj1mBBdOPaGuygTLIcszqLHAqohYDSBpLjAJ6AyoiHgxXbet0A66UjJD4snA2emim4DLcECZWYU1WplgOWQZUEOBl/OetwLjevH63SS1AG3AFRFxL7A/8EZEtOXt0/dmmlnFNGqZYDlkGVCF/rkQBZYVMzwi1kn6MLBA0tPAmwW2K7hPSTOBmQDDh/tOGTMbeI1cJlgOWQZUK3Bo3vNhwLoi2+4kItal/10taRHwUeBuYB9Jg9KzqKL7jIg5wByAXC7Xm2A0M+tWe/s27lvQ2GWC5ZBlQC0FRqV33a0FprH9s6NuSdoXeDsitko6APgT4NsREZIWApNJ7uSbAdyXyejNzAooVCY4aFAT0xusTLAcMguoiGiTdBEwn+Q28+sjYoWkWUBLRDRL+hhwD7AvcJqkyyPiKOAI4PvpzRO7kHwG1XFzxdeAuZK+CTwBXJfVezAz61CsTPAPDj2Qi6efzKEfbKwywXJQRP1f/crlctHS0lLpYZhZjVq/8S2uuW0hK1a5THAgSFoWEbmetvNMEmZmRUQEv1qSlAlufXfHMsEvTz+ZkcMat0ywHBxQZmYFFCsT/PwpH2XKxFzDlwmWgwPKzCyPywSrhwPKzCzlMsHq4oAyM8NlgtXIAWVmDc1lgtXLAWVmDctlgtXNAWVmDcdlgrXBAWVmDcVlgrXDAWVmDcFlgrXHAWVmdc9lgrXJAWVmdctlgrXNAWVmdcllgrXPAWVmdcVlgvXDAWVmdcNlgvXFAWVmNc9lgvXJAWVmNc1lgvXLAWVmNcllgvXPAWVmNadYmeAZE47lzE+PcZlgnXBAmVnNiAh+vWwVP5j3a5cJNgAHlJnVhE1vbWHOnQ+z5KkXdljuMsH65YAys6r36FMv8L0fPewywQaT6e0tkiZKek7SKkmXFlg/XtLjktokTc5bPlrSYkkrJD0laWreuhslvSBpefpndJbvwcwqZ/OWrVx16wK+fd38HcLplOOO4DuXnulwqnOZnUFJagJmAxOAVmCppOaIyC9geQk4D/hql5e/DXwxIlZKOgRYJml+RLyRrr8kIuZlNXYzqzyXCVqWl/jGAqsiYjWApLnAJKAzoCLixXTdtvwXRsTv8h6vk7QeOBB4AzOray4TtA5ZBtRQ4OW8563AuN7uRNJYYAjwfN7if5b0DeBXwKURsbXA62YCMwGGD/e/tsxqwYpV65h9+yKXCRqQbUAVmvQqCiwrvgPpYOAWYEZEdJxlfR34L5LQmgN8DZi10w+KmJOuJ5fL9ernmll5vfteG7f/9DF+uugplwlapywDqhU4NO/5MGBdkW13Imkv4H7g7yNiScfyiHglfbhV0g3s/PmVmdWQVWvWc9WtC1i7fvsVfJcJGmQbUEuBUZJGAmuBacDZpbxQ0hDgHuDmiLiry7qDI+IVJX9rTweeGdhhm1k5tLW1c9f8Zfz4wSdcJmgFZRZQEdEm6SJgPtAEXB8RKyTNAloiolnSx0iCaF/gNEmXR8RRwBRgPLC/pPPSXZ4XEcuB2yQdSHIJcTlwYVbvwcyy4TJBK4Ui6v/jmVwuFy0tLZUehlnDc5mgAUhaFhG5nrbzTBJmVhaFygQHD2riHJcJWhEOKDPLVLEywcOGH8TF009i2AdcJmiFOaDMLDMuE7T+cECZ2YDrrkzwK+eezIihLhO0njmgzGxAuUzQBooDyswGhMsEbaA5oMys31wmaFlwQJlZv7hM0LLigDKzPtm8ZSvX3f0bHlr6ux2WT/jEEcyYdBy77zakQiOzeuGAMrNec5mglYMDysxKVqxMcHxuFOd/wWWCNrAcUGZWkkJlgnvtsTszzzzeZYKWCQeUmXXLZYJWKQ4oMyvKZYJWSQ4oM9uJywStGjigzGwHLhO0auGAMjPAZYJWfRxQZuYyQatKDiizBuYyQatmDiizBuUyQat2DiizBuMyQasVmf4TSdJESc9JWiXp0gLrx0t6XFKbpMld1s2QtDL9MyNv+RhJT6f7vEq+OG5Wso2bNvPP3/8Z1859qDOcBHxhwrF8++++4HCyqpLZGZSkJmA2MAFoBZZKao6I/Em8XgLOA77a5bX7Af8I5IAAlqWvfR24FpgJLAF+BkwEHsjqfZjVA5cJWi3qNqAk3RgR56WPZ0TETb3Y91hgVUSsTl8/F5gEdAZURLyYrtvW5bWfBh6MiI3p+geBiZIWAXtFxOJ0+c3A6TigzIpymaDVqp7OoP447/FXgN4E1FDg5bznrcC4frx2aPqntcDynUiaSXKmxfDhnv7fGpPLBK2W9RRQ0cP67hT6bKjU/RV7bcn7jIg5wByAXC7Xn/dhVnM2b9nKD+f9modbVu6w3GWCVkt6Cqhhkq4iCYaOx50i4svdvLYVODR/X8C6ItsWeu2JXV67KF0+rI/7NGsILhO0etFTQF2S97ill/teCoySNBJYC0wDzi7xtfOBf5HU8S3BU4GvR8RGSW9J+jjwKPBF4OpejsusLrlM0OpNtwHVy5siur62TdJFJGHTBFwfESskzQJaIqJZ0seAe4B9gdMkXR4RR6VB9E8kIQcwq+OGCeBLwI3A7iQ3R/gGCWt4K1at45rbFrJ+41udy1wmaLVOEcU/npHU3N2LI+JzAz6iDORyuWhp6e0JoFn1K1YmOO4jI7lgyniXCVpVkrQsInI9bdfTJb7jSO6mu4Pkkpq/FGtWJVwmaPWup4D6IMkXbc8i+fzofuCOiFiR9cDMrDCXCVqj6OkzqHbg58DPJe1KElSLJM2KCN+cYFZma9Zt4MpbFrBm3YbOZS4TtHrV41RHaTD9KUk4jQCuAn6c7bDMLJ/LBK0R9TTV0U3A0SR3yl0eEc+UZVRm1sllgtaoejqDOhfYDBwOfEVSxwVvARER/mebWUYigvsfeppbf/KoywStIfX0GZQby8wqwGWCZj1f4tsNuBA4DHiK5Mu2beUYmFkjcpmg2XY9XeK7CXgPeAT4DHAUyazmZjbANm7azHfvWMQTv90+kb+AMyYcy5SJYxg0qKlygzOrgJ4C6siIOAZA0nXAY9kPyayxuEzQrLCeAqrzGkM6t17GwzFrLC4TNCuux8JCSW+mjwXsnj73XXxm/eQyQbPu9XQXny96mw0wlwmalabHmSTMbOA88duX+e4dLhM0K4UDyqwM3tn6Hjfe+x88+B+/3WG5ywTNinNAmWXMZYJmfeOAMsvIu++1cdtPHuP+h3YsExx7zAgunHqCywTNeuCAMsvAyjWvcvWtC10maNYPDiizAeQyQbOB44AyGyAuEzQbWA4os35qb9/GvQuW86MHWlwmaDaAMg0oSROBK4Em4IcRcUWX9bsCNwNjgA3A1Ih4UdI5wCV5m34EODYilktaBBwMdHz9/tSIWI9ZBbhM0Cw7mQWUpCZgNjABaAWWSmqOiGfzNjsfeD0iDpM0DfgWSUjdBtyW7ucY4L6IWJ73unMioiWrsZv1xGWCZtnL8gxqLLAqIlYDSJoLTALyA2oScFn6eB5wjSRFRP5duWcBd2Q4TrNeeXXDm8y+fZHLBM0ylmVADQVeznveCowrtk06W/omYH/gtbxtppIEWb4bJLUDdwPf7BJoZpmICH65+LfccM9ilwmalUGWAVXo4nvXIOl2G0njgLcj4pm89edExFpJe5IE1Lkkn2PtuGNpJjATYPhwz3Fm/bPhjd9z7dyHCpYJnvnpMQwe7HmVzQZalgHVChya93wYsK7INq2SBgF7Axvz1k+jy+W9iFib/vctSbeTXErcKaAiYg4wByCXy/kMy/rEZYJmlZNlQC0FRkkaCawlCZuzu2zTDMwAFgOTgQUdl+sk7QKcCYzv2DgNsX0i4jVJg4HPAr/M8D1YA3OZoFllZRZQ6WdKFwHzSW4zvz4iVkiaBbRERDNwHXCLpFUkZ07T8nYxHmjtuMkitSswPw2nJpJw+kFW78Eal8sEzSpPjXB/QS6Xi5YW35VuPfv921u57u6dywRPOe4IzjvdZYJmA0HSsojI9bSdZ5IwS7lM0Ky6OKCs4W15511uum/xTmWCx48ZxV9MdpmgWaU4oKyhFSoT3PP9u3HBlPEuEzSrMAeUNSSXCZpVPweUNRyXCZrVBgeUNQyXCZrVFgeUNQSXCZrVHgeU1TWXCZrVLgeU1S2XCZrVNgeU1R2XCZrVBweU1RWXCZrVDweU1QWXCZrVHweU1TyXCZrVJweU1ayI4JFlK/nBXb/m7Xfe7VzuMkGz+uCAsprkMkGz+ueAsprjMkGzxuCAsprhMkGzxuKAsprgMkGzxuOAsqrmMkGzxuWAsqrlMkGzxuaAsqrjMkEzAweUVZmVa17lqlsWsO6/N3Uuc5mgWWPKNKAkTQSuBJqAH0bEFV3W7wrcDIwBNgBTI+JFSSOA3wLPpZsuiYgL09eMAW4Edgd+BnwlIvL/oW01qK2tnTt/vox7fukyQTNLZBZQkpqA2cAEoBVYKqk5Ip7N2+x84PWIOEzSNOBbwNR03fMRMbrArq8FZgJLSAJqIvBARm/DysBlgmZWSJZnUGOBVRGxGkDSXGASkB9Qk4DL0sfzgGvUzW8jSQcDe0XE4vT5zcDpOKBqkssEzaw7WQbUUODlvOetwLhi20REm6RNwP7pupGSngDeBP4+Ih5Jt2/tss+CUwdImklypsXw4f6eTLVxmaCZ9STLgCr0G6brZ0XFtnkFGB4RG9LPnO6VdFSJ+0wWRswB5gDkcjl/RlUlXCZoZqXKMqBagUPzng8D1hXZplXSIGBvYGN608NWgIhYJul54PB0+2E97NOq1Ksb3uSa2xby7POvdC5zmaCZFZNlQC0FRkkaCawFpgFnd9mmGZgBLAYmAwsiIiQdSBJU7ZI+DIwCVkfERklvSfo48CjwReDqDN+DDQCXCZpZX2QWUOlnShcB80luM78+IlZImgW0REQzcB1wi6RVwEaSEAMYD8yS1Aa0AxdGxMZ03ZfYfpv5A/gGiarWXZnglIljGDTIZYJmVpga4StEuVwuWlpaKj2MhuIyQTMrRtKyiMj1tJ1nkrAB112Z4DmnjWXIYP+1M7Oe+TeFDaglT67m+3c+4jJBM+s3B5QNiGJlghM+cQQzJrlM0Mx6zwFl/eYyQTPLggPK+qxYmeD43CjO/4LLBM2sfxxQ1ieFygT32mN3Zp55vMsEzWxAOKCsV4qVCX78IyOZOWW8ywTNbMA4oKxkxcoE//LMT3L8GJcJmtnAckBZj4qVCX70iKRMcP99XCZoZgPPAWXdKlYm+GefP45TjnOZoJllxwFlBXVXJnjROSfxgf1dJmhm2XJA2U6KlQlOP20cf3qCywTNrDwcUNapWJngqA8dxMXTT2boQftUcHRm1mgcUAa4TNDMqo8DqsEVKxP80CH78+XpJ7lM0MwqxgHVwFwmaGbVzAHVgFwmaGa1wAHVYAqVCQr4U5cJmlmV8W+jBlKoTPCg/fbkonNO4qjDDqngyMzMduaAagAuEzSzWuSAqnOFygT32/v9fGnaCS4TNLOqlmlASZoIXAk0AT+MiCu6rN8VuBkYA2wApkbEi5ImAFcAQ4B3gUsiYkH6mkXAwUDHdapTI2I9tgOXCZpZrcssoCQ1AbOBCUArsFRSc0Q8m7fZ+cDrEXGYpGnAt4CpwGvAaRGxTtLRwHxgaN7rzomIlqzGXuuKlQleMOV4Pv7HLhM0s9qQ5RnUWGBVRKwGkDQXmATkB9Qk4LL08TzgGkmKiCfytlkB7CZp14jYmuF4a57LBM2snmQZUEOBl/OetwLjim0TEW2SNgH7k5xBdfgC8ESXcLpBUjtwN/DNiMj/fQyApJnATIDhw+v/sxaXCZpZvckyoAr9RuwaJN1uI+kokst+p+atPyci1krakySgziX5HGvHnUTMAeYA5HK5nQKsXnSUCf74wcd3OLguEzSzWpdlQLUCh+Y9HwasK7JNq6RBwN7ARgBJw4B7gC9GxPMdL4iItel/35J0O8mlxJ0CqhG8uPY1rrp1ocsEzawuZRlQS4FRkkYCa4FpwNldtmkGZgCLgcnAgogISfsA9wNfj4jfdGychtg+EfGapMHAZ4FfZvgeqlJ7+zbu+dVy7vy5ywTNrH5lFlDpZ0oXkdyB1wRcHxErJM0CWiKiGbgOuEXSKpIzp2npyy8CDgP+QdI/pMtOBTYD89NwaiIJpx9k9R6qUeurr3P1rQtZ9ZLLBM2svqnA/QV1J5fLRUtLbd+VHhH8dNHT3PZTlwmaWW2TtCwicj1t55kkakCxMsGp/yPH6Se7TNDM6pMDqop1Vyb4lXNP5kOH7F/B0ZmZZcsBVaUKlQnuInHGhI9y5qddJmhm9c8BVWW6KxP88rknM+pDLhM0s8bggKoiLhM0M9vOv/GqhMsEzcx25ICqMJcJmpkV5oCqIJcJmpkV54CqAJcJmpn1zAFVZi4TNDMrjQOqTFwmaGbWOw6oMnCZoJlZ7zmgMuQyQTOzvnNAZWTNug1cecsClwmamfWRA2qAtbdv494Fy/nRAy4TNDPrDwfUAFq7/g2uvnUBK9e4TNDMrL8cUAMgIrj/oae59ScuEzQzGygOqH4qViY4ZWKOz3/KZYJmZn3lgOqj7soEvzz9JEYMPaCCozMzq30OqD4oVCYo4IwJxzJlossEzcwGggOqF7orE7x4+skcPsJlgmZmA8UBVSKXCZqZlVemn+BLmijpOUmrJF1aYP2ukn6Urn9U0oi8dV9Plz8n6dOl7jMLS55czf+84s4dwumg/fbk8os/x5+d8QmHk5lZBjL7zSqpCZgNTABagaWSmiPi2bzNzgdej4jDJE0DvgVMlXQkMA04CjgE+KWkw9PX9LTPAeMyQTOzysnyn/5jgVURsRpA0lxgEpAfJpOAy9LH84BrlHybdRIwNyK2Ai9IWpXujxL2OSAigm9+7/4dvnS7717v46/OOtFlgmZmZZBlQA0FXs573gqMK7ZNRLRJ2gTsny5f0uW1Q9PHPe0TAEkzgZnp099Leq4P76HDAcBrANd9sx97qW2dx6CB+Rj4GICPAfT/GHyolI2yDKhC8/pEidsUW17oM7Ou+0wWRswB5nQ3wFJJaomI3EDsq1b5GPgYgI8B+BhA+Y5BljdJtAKH5j0fBqwrto2kQcDewMZuXlvKPs3MrA5kGVBLgVGSRkoaQnLTQ3OXbZqBGenjycCCiIh0+bT0Lr+RwCjgsRL3aWZmdSCzS3zpZ0oXAfOBJuD6iFghaRbQEhHNwHXALelNEBtJAod0uztJbn5oA/46ItoBCu0zq/eQZ0AuFdY4HwMfA/AxAB8DKNMxUHLCYmZmVl081baZmVUlB5SZmVUlB1Se/kzNVC9KOAZ/K+lZSU9J+pWkkr7PUEtKnU5L0mRJIanubjku5RhImpL+XVgh6fZyjzFrJfy/MFzSQklPpP8/fKYS48yKpOslrZf0TJH1knRVenyeknTsgA8iIvwn+RyuCXge+DAwBHgSOLLLNn8FfC99PA34UaXHXYFjcBLwvvTxlxrxGKTb7Qk8TPKF8lylx12BvwejgCeAfdPnB1V63BU4BnOAL6WPjwRerPS4B/gYjAeOBZ4psv4zwAMk31v9OPDoQI/BZ1DbdU7NFBHvAh3TKOWbBNyUPp4HfCqdmqle9HgMImJhRLydPl1C8l20elLK3wOAfwK+DbxTzsGVSSnH4C+B2RHxOkBErKe+lHIMAtgrfbw3dfadzIh4mOTu6mImATdHYgmwj6SDB3IMDqjtCk3NNLTYNhHRBnRMzVQvSjkG+c4n+RdUPenxGEj6KHBoRPy0nAMro1L+HhwOHC7pN5KWSJpYttGVRynH4DJguqRW4GfAxeUZWtXo7e+LXnNPxHb9mZqpXpT8/iRNB3LACZmOqPy6PQaSdgG+A5xXrgFVQCl/DwaRXOY7keQs+hFJR0fEGxmPrVxKOQZnATdGxL9JOo7kO51HR8S27IdXFTL/fegzqO36MzVTvShpKilJpwD/G/hcJDPO15OejsGewNHAIkkvklx7b66zGyVK/X/hvoh4LyJeAJ4jCax6UcoxOB+4EyAiFgO7kUyi2igyn3rOAbVdf6Zmqhc9HoP08tb3ScKp3j53gB6OQURsiogDImJERIwg+RzucxHRUpnhZqKU/xfuJblhBkkHkFzyW13WUWarlGPwEvApAElHkATUf5d1lJXVDHwxvZvv48CmiHhlIH+AL/Gloh9TM9WLEo/B/wX2AO5K7w95KSI+V7FBD7ASj0FdK/EYzAdOlfQs0A5cEhEbKjfqgVXiMfg74AeS/obk0tZ59fQPVkl3kFzCPSD9nO0fgcEAEfE9ks/dPgOsAt4G/mzAx1BHx9PMzOqIL/GZmVlVckCZmVlVckCZmVlVckCZmVlVckCZmVlVckCZlUhSu6Tlkp6R9BNJ+5Twmt+n/z1E0rwetj1RUsHpkyT9rOPnFdqnpNF9mU1b0umSvpE+vkzS2vQ9PivprLzt/lXSyb3dv1l/OKDMSrclIkZHxNEk34P761JfGBHrImJyX39wRHym6zRCXfY5muQ7Kb31v4Dv5j3/TkSMJpkI9PuSBqfLrwaKVo+YZcEBZdY3i8mbGFPSJZKWpr04l3fdWNKIjl6d9PEjkh5P/3wib9O9JN2TnsF8L537D0kvpjM27LTPdKaDWcDU9OxnqqSVkg5Mt9sl7ezp+vrDga0R8VrX8UbESpIvX+6bPl8D7C/pg305WGZ94YAy6yVJTSRT3DSnz08lmYduLMmZzBhJ47vZxXppmGe6AAACDElEQVRgQkQcC0wFrspbN5ZkhoJjgD8AzuhpPGkdxDdIurlGR8SPgFuBc9JNTgGeLBBEfwI8XuQ9Hgus7DKd1ePpa8zKwgFlVrrdJS0HNgD7AQ+my09N/zxB8kv8j+h+4tTBJFPkPA3cRVJ21+GxtIOoHbgD+GQfx3o98MX08Z8DNxTY5mB2njvubyQ9BzxKUieRbz1wSB/HY9ZrDiiz0m1JP5/5EEnLasdnUAL+T3r2MjoiDouI67rZz98ArwJ/TFJZMiRvXde5x/o0F1lEvAy8mt7YMI7CvV1bSCY4zfediPhDkjO7myXlr98tfY1ZWTigzHopIjYBXwa+mt5EMB/4c0l7AEgaKumgbnaxN/BK2ht0LslkpB3GpjNo70ISEr8ucVhvkVSB5PshyaW+O9Mzsq5+CxxWaGcR8WOghe2z90MyY/kzJY7HrN8cUGZ9EBFPAE8C0yLiF8DtwOL0st08dg6LfN8FZkhaQvJLf3PeusXAFSRB8AJwT4lDWggc2XGTRLqsmWTm+UKX9wAeBj6qdFr6AmYBf5veZDGYJMzqqVbEqpxnMzerU2mJ4nci4vhutrkS+ElE/LKHfX0eODYi/mGAh2lWlM+gzOqQpEuBu4Gv97DpvwDvK2GXg4B/6++4zHrDZ1BmZlaVfAZlZmZVyQFlZmZVyQFlZmZVyQFlZmZVyQFlZmZV6f8Dib2qmpRKxn4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.Pdf(redditor_post)\n", "ylim = 0, redditor_post.MaxLike() * 1.05\n", "thinkplot.decorate(xlabel='Reliability (R)',\n", " ylabel='PMF', ylim=ylim)\n", "\n", "mean_r = redditor_post.Mean()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "item_post = Pmf()\n", "for (q, r), p in suite.Items():\n", " item_post[q] += p\n", " \n", "item_post.Total()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5800000000000001" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.Pdf(item_post)\n", "ylim = 0, item_post.MaxLike() * 1.05\n", "thinkplot.decorate(xlabel='Quality (Q))',\n", " ylabel='PMF', ylim=ylim)\n", "\n", "mean_q = item_post.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 }