{ "metadata": { "name": "Bayes-1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from thinkbayes import Pmf" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "pmf = Pmf()\n", "for x in range(1, 7):\n", " pmf.Set(x, 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "pmf.Print()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 1\n", "2 1\n", "3 1\n", "4 1\n", "5 1\n", "6 1\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "pmf.Normalize()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 4, "text": [ "6" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "pmf.Print()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 0.166666666667\n", "2 0.166666666667\n", "3 0.166666666667\n", "4 0.166666666667\n", "5 0.166666666667\n", "6 0.166666666667\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Prior" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pmf = Pmf()\n", "pmf.Set('Bowl 1', 0.5)\n", "pmf.Set('Bowl 2', 0.5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Update" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "P(Vanilla | Bowl 1) = 3/4\n", "\n", "P(Vanilla | Bowl 1) = 2/4" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pmf.Mult('Bowl 1', 0.75)\n", "pmf.Mult('Bowl 2', 0.5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Normalize" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pmf.Normalize()\n", "print pmf.Prob('Bowl 1')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.6\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The dice problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We representa box with dice of [4, 6, 8, 12, 20] sides." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from thinkbayes import Suite\n", "\n", "# Start with equal priors\n", "suite = Suite([4, 6, 8, 12, 20])\n", "suite.Print()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4 0.2\n", "6 0.2\n", "8 0.2\n", "12 0.2\n", "20 0.2\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to add a `Likelihood` method" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from thinkbayes import Suite\n", "\n", "class Dice(Suite):\n", " def Likelihood(self, hypo, data):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " hypo: integer number of sides on the die\n", " data: integer die roll\n", " \"\"\"\n", " num_sides = hypo\n", " outcome = data\n", "\n", " if outcome > num_sides:\n", " return 0\n", " else:\n", " return 1.0/num_sides" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's build a suite that represents our dice:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "suite = Dice([4, 6, 8, 12, 20])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's say that we roll *once* and get a 6. What does this tells us about the probabilities for the various dice?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "suite.Update(6)\n", "print 'After one 6'\n", "suite.Print()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "After one 6\n", "4 0.0\n", "6 0.392156862745\n", "8 0.294117647059\n", "12 0.196078431373\n", "20 0.117647058824\n" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now let's roll a few more dice and figure out how that informs us about the probability of the die being of a specific size:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for roll in [6, 4, 8, 7, 7, 2]:\n", " suite.Update(roll)\n", " \n", "suite.Print()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4 0.0\n", "6 0.0\n", "8 0.943248453672\n", "12 0.0552061280613\n", "20 0.0015454182665\n" ] } ], "prompt_number": 23 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The German Tank problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ref: [Wikipedia page on the German Tank problem](http://en.wikipedia.org/wiki/German_tank_problem)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n", "For more information, type 'help(pylab)'.\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "import thinkbayes\n", "import myplot\n", "\n", "class Train(thinkbayes.Suite):\n", " \"\"\"Suite of hypotheses about the number of trains.\"\"\"\n", "\n", " def Likelihood(self, hypo, data):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " hypo: integer hypothesis about the number of trains\n", " data: integer serial number of the observed train\n", " \"\"\"\n", " num_trains = hypo\n", " train_seen = data\n", " if train_seen > num_trains:\n", " return 0\n", " else:\n", " return 1.0/num_trains" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Given the data, our mean estimate is: 597.344008084\n" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEACAYAAAD1KqK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlB4dwwAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "hypos = xrange(100, 1001)\n", "suite = Train(hypos)\n", "\n", "suite.Update(321)\n", "print 'Given the data, our mean estimate is:',\n", "print suite.Mean()\n", "\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Given the data, our mean estimate is: 597.344008084\n" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAEACAYAAAD1KqK3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVHX+P/DXcJkBFFkN5TJ845ZKShcFScgLqyt52dxv\nu2aYWVpmbqWiuSqGeSO72JJWUizud2Pb7/7ETbK2ywaFEAheSEkEsgy8pTN9KcUsAcH3748TM4yM\nMCBwBnw9H4/zcOZz3ufMewacN59zPudzNCIiICIi6mIOaidARETXJxYgIiJSBQsQERGpggWIiIhU\nwQJERESqYAEiIiJV2FSAkpOTERgYCFdXV4SHhyM/P7/F+JKSEowdOxZubm7w8/PD+vXrm8Xk5uYi\nLCwMrq6uCA4ORkpKisX6f/3rXwgPD0ffvn3Ru3dvDBs2DH//+9+vOTciIrIT0opt27aJs7OzbN26\nVb788ktZsGCB9O7dW06cOGE1vrq6Wry8vOS+++6T0tJSefvtt8Xd3V3+/Oc/m2IqKirEzc1NFi5c\nKF9++aWkpqaKs7Oz7NixwxSTnZ0t7777rhw5ckQqKipk8+bN4uTkJO+//367cyMiIvvRagGKiIiQ\nefPmWbQNHDhQ4uPjrcYnJyeLh4eH1NTUmNoSExNFr9ebni9btkwGDRpksd3cuXMlMjKyxVyGDx8u\nK1eubHduRERkP1o8BFdXV4cDBw4gJibGoj0mJgYFBQVWtyksLMTo0aOh0+ks4k+fPo3jx4+bYqzt\ns6ioCA0NDdZ6afj000/x1VdfYfz48e3OjYiI7IdTSyurqqrQ0NAALy8vi/YBAwbAYDBY3cZgMODG\nG2+0aGvc3mAwwN/fH0ajsdk+vby8UF9fj6qqKtO66upq6PV61NXVQaPRYMuWLRg3blybc9NoNC29\nTSIiugrpxNnaOnwUXEd+2ffp0weHDh1CUVERnnvuOSxatAjvvfdeu/YlyuFGu1lWr16teg7dJS/m\nxJyuh7zsMafO1mIPyNPTE46OjjAajRbtRqMRPj4+Vrfx9vZu1gNp3N7b27vFGCcnJ3h6epraNBoN\ngoKCAAC33norysvL8fLLL2Pq1Kntyo2IiOxHiz0grVaLsLAwZGZmWrRnZWUhKirK6jaRkZHIy8tD\nbW2tRbxer4e/v78pJisrq9k+R4wYAUdHx6vm09DQgMuXL7c7NyIisiPSivT0dNFqtbJ161YpKyuT\nhQsXiru7u2mo84oVK2T8+PGm+OrqavH29pbY2Fg5fPiw7NixQ/r06SNJSUmmmMrKSunVq5fExcVJ\nWVmZpKamilarlYyMDFNMYmKifPLJJ/LNN99IWVmZvPTSS+Ls7Cypqak259bIhrfZ5Xbt2qV2ClbZ\nY17MyTbMyXb2mJc95tTZ35027T05OVkCAgJEp9NJeHi45OXlmdbNnj1bAgMDLeJLSkpkzJgx4uLi\nIr6+vrJu3bpm+8zNzZXhw4eLTqeToKAgSUlJsVgfHx8vAwcOFFdXV+nXr5/ceeedsm3btjblZnqT\ndliAiIjsXWd/d2p+eZEeTaPRdMkJNSKinqSzvzs5FxwREamCBYiIiFTBAkRERKpgASIiIlWwABER\nkSpYgIiISBUsQEREpAoWICIiUgULEBERqYIFiIiIVMECREREqmABIpv9/DNw6BBQX692JkTUE3Ay\nUrKJCPDAA8CRI8CYMcCf/wzwTudEPRsnIyW7cPasUnwA4LPPgI8/VjcfIur+WIDIJlf+EbRpE3Dh\ngjq5EFHPwAJENrmyAFVVASkp6uRCRD0DCxC1W3q6+bAcEVFbsQCRTaydh7x8GXjhBeVfIqK2YgGi\nNtPpACcn5fGhQ8D776ubDxF1TyxAZJOmPSB3d2DWLPPzV14Bfvih63Miou6NBYja5ZFHAB8f5fG5\nc8p1QUREbcECRDZp2gPSaAAXFyA+3tz28cfK9UFERLZiAaJ2i4oCpkwxP3/+eV4bRES2YwGiNms6\nBc+SJUC/fsrj774DXn1VnZyIqPthAaJr4uEBLFtmfr5jB/D55+rlQ0TdBwsQtdmVk5COHw9ER5uf\nr18PXLzYpSkRUTdkUwFKTk5GYGAgXF1dER4ejvz8/BbjS0pKMHbsWLi5ucHPzw/r169vFpObm4uw\nsDC4uroiODgYKVfM65KamorRo0ejX79+6Nu3L8aNG4fdu3dbxKxZswYODg4Wi6+vry1vidqopQlx\nNRpg+XKgd2/l+alTylxxREQtabUApaenIy4uDgkJCSguLkZUVBQmTZqEkydPWo0/f/48JkyYAB8f\nHxQVFWHz5s3YuHEjkpKSTDGVlZWYPHkyRo0aheLiYsTHx2PBggXIyMgwxeTm5mLGjBnYtWsX9u7d\ni8GDB+Ouu+7C0aNHLV4vJCQEBoPBtJSUlLT3s6Br0L8/sHSp+fmOHUBBgXr5EFE3IK2IiIiQefPm\nWbQNHDhQ4uPjrcYnJyeLh4eH1NTUmNoSExNFr9ebni9btkwGDRpksd3cuXMlMjKyxVy8vb3ltdde\nMz1fvXq1hIaGtvYWxIa3Sa04fVokLExZpkyxHnP5ssjSpea4u+4SOXeua/Mkoo7T2d+dLfaA6urq\ncODAAcTExFi0x8TEoOAqf94WFhZi9OjR0Ol0FvGnT5/G8ePHTTHW9llUVISGhgar+62trUVNTQ36\n9u1r0V5RUQG9Xo+goCDMmDEDlZWVLb0l6kQaDbByJXDDDcrzqirguedaPnxHRNcvp5ZWVlVVoaGh\nAV5eXhbtAwYMgMFgsLqNwWDAjTfeaNHWuL3BYIC/vz+MRmOzfXp5eaG+vh5VVVXN1gFAQkIC3N3d\nMXXqVFPbyJEjkZaWhpCQEBiNRiQmJiIqKgqlpaXo1zg2+Bdr1qwxPY6OjkZ007Pm1Cpbi0jfvkBC\nArB4sfL8k0+AsWOBSZM6Lzci6hg5OTnIycnpstdrsQC1h6YT7tO8efNm/OUvf8Gnn36K3o1nugFM\nnDjR9Dg0NBSRkZEIDAxEWloaFjd+A/6iaQGizjV6NPDf/w3s3Kk8f+EF4LbbAI4PIbJvV/5xvnbt\n2k59vRYPwXl6esLR0RFGo9Gi3Wg0wqdxIrAreHt7N+sdNW7v7e3dYoyTkxM8PT0t2jdt2oRVq1bh\no48+Qnh4eItvxs3NDUOHDm02UIGu3ZVT8bRm8WJAr1ceX7ig9Irq6zsnNyLqnlosQFqtFmFhYcjM\nzLRoz8rKQlRUlNVtIiMjkZeXh9raWot4vV4Pf39/U0xWVlazfY4YMQKOjo6mtqSkJDzzzDP48MMP\nr/p6TdXU1KC8vPyqxZG6Tq9eyvVAjT/OQ4eAN95QNycisjOtjVJIT08XrVYrW7dulbKyMlm4cKG4\nu7vLiRMnRERkxYoVMn78eFN8dXW1eHt7S2xsrBw+fFh27Nghffr0kaSkJFNMZWWl9OrVS+Li4qSs\nrExSU1NFq9VKRkaGKebFF18UrVYr27dvlzNnzpiW6upqU8xTTz0lubm5UlFRIXv27JEpU6aIh4eH\nKbdGNrxNasWpU+bRbXffbft2b75p3i4sTKSwsPNyJKKO1dnfnTbtPTk5WQICAkSn00l4eLjk5eWZ\n1s2ePVsCAwMt4ktKSmTMmDHi4uIivr6+sm7dumb7zM3NleHDh4tOp5OgoCBJSUmxWB8QECAODg6i\n0Wgsljlz5phiYmNjxdfXV7Rarej1epk2bZqUl5c3f5MsQNesvQWooUHkiSfM206YIFJV1Xl5ElHH\n6ezvTs0vL9KjaTQaXAdvs1OdOqUMLACUczvvvmv7tt9/D8ycqQzLBoA77lAmLXXgRFBEdq2zvzv5\nFUCd7oYbgHXrzIMX9u4F/ud/1M2JiNTHAkQ2aesouCtFRABz5pifp6Rwqh6i6x0LEHWZxx4DwsKU\nxyLK0Oxvv1U3JyJSDwsQdRlHR2VqngEDlOfnzwN/+hNQU6NuXkSkDhYgsklHnYfs1w948UXA2Vl5\n/tVXwIYNnC+O6HrEAkRdLjRU6fk0+vBDYPt29fIhInWwAJFNrnUQwpXuuQf43e/Mz5OSgP37r32/\nRNR9sACRKjQaYNkyYOhQ5XlDg/L8lzt2ENF1gAWI2qyjJjzX6YCNG5W7qQLAjz8qk5hWV3fM/onI\nvrEAkaoGDAD+/GfAxUV5fuIEsGIFZ84muh6wAJFNOvocUFNDhgBNbzuyf78yUo4j44h6NhYgsgvj\nxwN//KP5eUYG8I9/qJcPEXU+FiCySWf2gBo9/DDQ5Ca32LwZ+M9/Oue1iEh9LEBkNzQaYNUqYNgw\nc9uaNcC+faqlRESdiAWIbNJV52N0OmVQQlCQ8ry+Xrlo9ciRrnl9Iuo6LEBkd/r0Ue4X1Dhn3E8/\nAQsXcuJSop6GBYjarLPOATXl5aUUod69lefffw8sWACcPdv5r01EXYMFiGyixpDo4GDlcFzjxKUn\nTgBPPqnMok1E3R8LENm1sDAgMdF8++4jR4BFi5TDckTUvbEAUZt1xSG4psaPV25e16ikBFiyBKit\n7do8iKhjsQBRtzB1qjJZaaPPP1eeX7qkXk5EdG1YgMgmXXEhamumT1cGIjTavRt4+mnOG0fUXbEA\nUbfy0EPA3Lnm59nZwMqV7AkRdUcsQGQTe+gBNXrsMWDmTPPz7GxlBm0WIaLuhQWIuh2NBoiLsyxC\nubnKOaG6OvXyIqK2YQEim9jbrREai9CDD5rb8vKApUs5Oo6ou7CpACUnJyMwMBCurq4IDw9Hfn5+\ni/ElJSUYO3Ys3Nzc4Ofnh/Xr1zeLyc3NRVhYGFxdXREcHIyUlBSL9ampqRg9ejT69euHvn37Yty4\ncdi9e/c150Y9h0ajDEqYM8fcVlCgDNH++Wf18iIi27RagNLT0xEXF4eEhAQUFxcjKioKkyZNwsmT\nJ63Gnz9/HhMmTICPjw+KioqwefNmbNy4EUlJSaaYyspKTJ48GaNGjUJxcTHi4+OxYMECZGRkmGJy\nc3MxY8YM7Nq1C3v37sXgwYNx11134ejRo+3OjdrPns4BNaXRAI8/bjkwYe9epY239iayc9KKiIgI\nmTdvnkXbwIEDJT4+3mp8cnKyeHh4SE1NjaktMTFR9Hq96fmyZctk0KBBFtvNnTtXIiMjW8zF29tb\nXn311TbnZsPbpFZ8+aVIWJiyxMaqnY11W7eacwwLE5k+XcRoVDsrou6rs787W+wB1dXV4cCBA4iJ\nibFoj4mJQUFBgdVtCgsLMXr0aOh0Oov406dP4/jx46YYa/ssKipCQ0OD1f3W1taipqYG/fr1a3du\n1DHsqQfU1COPWF6s+s03Ss/oxAn1ciKiq3NqaWVVVRUaGhrg5eVl0T5gwAAYDAar2xgMBtx4440W\nbY3bGwwG+Pv7w2g0Ntunl5cX6uvrUVVV1WwdACQkJMDd3R1Tp05tV25r1qwxPY6OjkZ0dLT1N03d\n2vTpyu0c1qxRLlA9fVopQq++CgwerHZ2RPYtJycHOTk5XfZ6LRag9tB0wp/Hmzdvxl/+8hd8+umn\n6N04P38bNS1AdG3stQfUaOJEwN0dWL4cqKkBfvhBuXboxReBiAi1syOyX1f+cb527dpOfb0WD8F5\nenrC0dERRqPRot1oNMLHx8fqNt7e3s16II3be3t7txjj5OQET09Pi/ZNmzZh1apV+OijjxAeHn5N\nuVH72dsw7NbceSfw2mvm+wlduKDc1O7f/1Y3LyIya7EAabVahIWFITMz06I9KysLUVFRVreJjIxE\nXl4eaptcjJGVlQW9Xg9/f39TTFZWVrN9jhgxAo6Ojqa2pKQkPPPMM/jwww+bvV57cqPry+23A6mp\n5jur1tcDa9cCKSndr6AS9UitjVJIT08XrVYrW7dulbKyMlm4cKG4u7vLiRMnRERkxYoVMn78eFN8\ndXW1eHt7S2xsrBw+fFh27Nghffr0kaSkJFNMZWWl9OrVS+Li4qSsrExSU1NFq9VKRkaGKebFF18U\nrVYr27dvlzNnzpiW6upqm3NrZMPbpFaUlZlHl82cqXY2bWMwKCP3mo6Qe+YZkbo6tTMjsm+d/d1p\n096Tk5MlICBAdDqdhIeHS15enmnd7NmzJTAw0CK+pKRExowZIy4uLuLr6yvr1q1rts/c3FwZPny4\n6HQ6CQoKkpSUFIv1AQEB4uDgIBqNxmKZM2eOzbmZ3iQL0DXrzgVIROTCBZEnn7QsQvPmiZw7p3Zm\nRPars787Nb+8SI+m0WhwHbzNTlVWZp72JiQE+Mc/1M2nPerrgeefB3buNLf5+Sm3/Q4OVi8vInvV\n2d+dnAuOrhtOTsr9g5580tx26pQylU9urnp5EV2vWIDIJvY6FU9baTTA7NnKkGxXV6Xt55+Bp54C\n/vpXDk4g6kosQHRdGjcO+NvfAL3e3Pb660B8PHDxonp5EV1PWICozbpzD6ipm24C0tKAJpeX4ZNP\nlENyv8waRUSdiAWIrmu/+pVywep995nbjh5VBlx8+ql6eRFdD1iAyCY95RyQNU5OwJ/+BDzzDKDV\nKm0//aRM5fPnP/NW30SdhQWI6BdTpyrnhfz8zG3/7/8B8+YBV5l7l4iuAQsQ2aQn94CaGjwYeOst\nYOxYc1tJCTBzpnLLbyLqOCxARFdwdwdeeglYtAhonJqwuhpYvFgZvt1kmkMiugYsQERWaDTArFnA\nG28A/fub27dvVwYoNLkzPBG1EwsQ2eR6vUBz2DDlPFDTQ3LffKMUofT06/dzIeoILEBErfjVr5RD\ncitXAi4uSltdHbBxIxAXB3z/vbr5EXVXLEBkk+tlEMLVaDTA73+vDFAYNMjcvnu3cg1RZiZ7Q0Rt\nxQJE1AaBgcCbbyqj4hqdO6f0jlasAM6eVS01om6HBYja7HrsATWl1Soj4pKTgV/uMg9AmTlh+nTO\noEBkKxYgonaKiFAGItxzj7nt7FllBoWVK5WeERFdHQsQ2eR6Pwd0Nb16KfcYevVVYMAAc3tmJjBt\nGvDhhzw3RHQ1LEBEHSAyUukNTZ1qbjt3Tplf7okngJMn1cuNyF6xAJFN2ANqnbu7UnA2b7Y8N7Rv\nHxAbC/zP/3BiU6KmWICIOtiddyozJsycCTj88j+stlYZtDBrFvDFF+rmR2QvWIDIJjyP0TZubspI\nubQ0ICTE3H70KPDII8CaNbyAlYgFiKgT3XyzUoSWLAFcXc3t77+vXNj6v/8L1Nerlx+RmliAqM14\nDqhtHB2B++9XDsv9+tfm9p9+Al5+WTk/tHevevkRqYUFiGzCQ3DXzsdHmT9uyxYgIMDcfuyYMlLu\nT38Cvv1WreyIuh4LELUZe0DX5o47lBm24+KU64ga7dqlXDv08svK/YeIejoWICIVODsDDzwA7NgB\nTJlibr90STkvdM89wD/+ocy6TdRT2VSAkpOTERgYCFdXV4SHhyM/P7/F+JKSEowdOxZubm7w8/PD\n+vXrm8Xk5uYiLCwMrq6uCA4ORkpKisX60tJSTJs2DcHBwXBwcMDatWub7WPNmjVwcHCwWHx9fW15\nS0R2wdMTWLtWuUbollvM7efPA5s2KT2ijz8GLl9WL0eiztJqAUpPT0dcXBwSEhJQXFyMqKgoTJo0\nCSevcmn3+fPnMWHCBPj4+KCoqAibN2/Gxo0bkZSUZIqprKzE5MmTMWrUKBQXFyM+Ph4LFixARkaG\nKebixYsICgpCYmIiAgMDobnKcZ+QkBAYDAbTUlJS0tbPgGzAC1E71623KkXohRcAPz9z++nTylQ/\nc+YARUXq5UfUKaQVERERMm/ePIu2gQMHSnx8vNX45ORk8fDwkJqaGlNbYmKi6PV60/Nly5bJoEGD\nLLabO3euREZGWt1naGiorF27tln76tWrJTQ0tLW3IDa8TWrF/v0iYWHKcsWvA3WwujqRf/5TZNw4\n82feuPzxjyKHDqmdIV0vOvu7s8UeUF1dHQ4cOICYmBiL9piYGBQUFFjdprCwEKNHj4ZOp7OIP336\nNI4fP26KsbbPoqIiNDQ0tKmAVlRUQK/XIygoCDNmzEBlZWWbtifbcBRc13F2BmbMAHbuBB56SLn9\nQ6N9+5TeUFwccOSIejkSdQSnllZWVVWhoaEBXl5eFu0DBgyAwWCwuo3BYMCNN95o0da4vcFggL+/\nP4xGY7N9enl5ob6+HlVVVc3WXc3IkSORlpaGkJAQGI1GJCYmIioqCqWlpejXr59F7Jo1a0yPo6Oj\nER0dbdNrEKnF3R1YsEA5D7R1q3LxauPfZ/n5yjJ+PPDYY0BQkLq5Us+Qk5ODnJycLnu9FgtQe1zt\nXE1nmDhxoulxaGgoIiMjERgYiLS0NCxevNgitmkBorbjOSD1+PgAq1YBDz4IpKYqgxIafx6ffgpk\nZwN33aX0jIKD1c2Vurcr/zi3NvirI7V4CM7T0xOOjo4wGo0W7UajET4+Pla38fb2btY7atze+5cp\ngq8W4+TkBE9Pz7a9gybc3NwwdOhQHD16tN37ILJX/v5AYiKwbRswbpy5XQT4z3+A++4Dli0DvvxS\nvRyJ2qLFAqTVahEWFobMzEyL9qysLERFRVndJjIyEnl5eaitrbWI1+v18Pf3N8VkZWU12+eIESPg\n6OjYrjcCADU1NSgvL79qcaSOwR6QuoKDgRdfBN56S5l5u6nsbOX6okWLOOs22b9Wh2EvWbIEb775\nJv7617+ivLwcixYtgsFgwPz58wEA8fHx+M1vfmOKv//+++Hm5obZs2ejtLQUGRkZeOGFF7BkyRJT\nzPz58/Htt99i8eLFKC8vx9atW5GWloalS5eaYi5duoTi4mIUFxfj4sWLOHPmDIqLiy16N0uXLsVn\nn32GyspK7N27F9OmTcPFixfx0EMPdciHQ2TPbr5ZufdQWhowdqzlut27lVm3H3tMGbjAQSRkl2wZ\nKpecnCwBAQGi0+kkPDxc8vLyTOtmz54tgYGBFvElJSUyZswYcXFxEV9fX1m3bl2zfebm5srw4cNF\np9NJUFCQpKSkWKyvrKwUjUYjGo1GHBwcTI9//etfm2JiY2PF19dXtFqt6PV6mTZtmpSXlzd7LRvf\nJrVgzx7LocBkf776SiQ+XiQ8vPnw7ZkzRT76SOTSJbWzpO6ks787Nb+8SI+m0WhwHbzNTrV3rzJh\nJgBERCg3VyP7dPw48Le/AR99ZB4118jbWxni/d//bTkPHZE1nf3dybngqM14Dsi++fsrN7zLyFCG\ncDe5JA8GgzLZ6ZQpyuG7K8YXEXUpFiCyCTuQ3Y9eD6xYoVw/9NhjQN++5nUXLiiDGH73OyAhASgp\n4c+Yuh4LEFEP17cv8OijSiF6+mnLexHV1ytDuOfMUa4z+ve/gSYDWIk6FQsQ2YR/HXd/Op1ym4ft\n25XDcGFhluvLy5WZuadMAV57DThzRp086frBAkR0nXFwAEaPBlJSlMNwU6dazjd37hzw5pvK4bml\nS5Vh3LwdBHUGjoIjmxQUAAsXKo9HjlT+Qqae49w54L33gH/9y3rPx89PKUhTpwI33ND1+ZE6OAqO\niDrdr36lnAPauRNISlJuG97UqVPAli3A5MlKr2j37uZDvInaqsMnI6Wej8Owey5HR2DMGGU5dkzp\nEX34IfDjj8r6hgYgJ0dZvL2VHtHUqcpjorbiITiySdNDcJGRwKuvqpsPdZ3aWmXW7XfeAQ4ebL5e\no1EOy06ZAkRHAy4uXZ4idZLO/u5kD4hswtsxXL90OuXQ2+TJSq9o505lSPe5c8p6EaCwUFl69QJ+\n8xsldtgwZcAD0dWwB0Q22b1bmWEZAKKigFdeUTcfUlddHZCbqxSjvXutx/j4mAvXLxPhUzfT2d+d\nLEBkk/x85TbQgHILgM2b1c2H7MeZM8q8c++/D5w4YT3m1luBiROVO7hyFF33wQLUAViArh0LELVG\nBCgtVQpRZiZw/nzzGAcHIDwciIkBfv1rwMOj6/Mk27EAdQAWoGuXlwc03uWcBYhaU1en/NHywQfK\n4dv6+uYxTk7KcO+77lLuZ8TZue0PByEQUbej1Sq3DR83Djh7FvjkEyArSxlF1/h9Vl+vFKfdu5X4\nUaOACROUP3Dc3NTNn7oGCxC1GUfBUVv07Qvce6+yGI3KkO7MTODwYXNMXZ1yO/HsbKUYRUYqQ7rH\njOFhup6MBYhswiOY1BG8vID771eWb79VekVZWcCRI+aYxhF2ubnKhbFhYUoxio4GBgxQK3PqDDwH\nRDb57DNgyRLl8ahRwKZN6uZDPcuxY0qvKDsbOHr06nGhocphveho4MYbuyq76xcHIXQAFqBr17QA\njR6tTOdP1BlOngR27VKWkpKrxwUEKH8MjR4N3HabMqiBOhYLUAdgAbp2LECkhu++Uw7F7doFfP75\n1SdAdXdXzhuNGqUMYuB5o47BUXBkFzgVD6lhwADzAIbqauVygOxsZfaFpndu/fFH5RBeZqZyrdGt\nt5p7R0FB/J21V+wBkU1yc4GnnlIejxmjTNlPpJbaWqCoSClI+fmAwXD1WG9v5XqjkSOBiAj2jtqC\nPSCyC+wBkT3R6ZRDbXfeqfxuHj1qLkYlJZa/rwYD8O67yqLRAEOHKsVo5EhlUAPPHamHHz0RdWsa\nDTBwoLI8/LBy4WthoVKQCguBCxfMsSLK9UeHDwNbtwK9eytTA40cqZxD0uvVex/XIxYgsgmPYFJ3\n0beveRbuhgal2OzZoyylpcDly+bYCxfMN9gDlAIUHm5e+vdX4x1cP1iAiKjHcnRUhmjfdhvw2GPK\nQIaiIqVntGdP83NH336rLO++qzwPCDAXo7AwpbhRx7HpdlHJyckIDAyEq6srwsPDkZ+f32J8SUkJ\nxo4dCzc3N/j5+WH9+vXNYnJzcxEWFgZXV1cEBwcjJSXFYn1paSmmTZuG4OBgODg4YO3atR2SG7UP\nzwFRT+DhodwSIiEB+Pe/gbffBpYuVc4lWbuT67FjSsyKFco8dbGxwEsvKYNyrM32TW3Tag8oPT0d\ncXFxeP311zFq1Chs2bIFkyZNQllZGf7rv/6rWfz58+cxYcIEREdHo6ioCOXl5ZgzZw569eqFJb9c\nSFJZWYnJkydj7ty5+Oc//4m8vDw8/vjj6N+/P37/+98DAC5evIigoCD84Q9/QEJCAjRWvvXamhsR\nUSONRulkhjKUAAAYbUlEQVThBAQoheXSJeUQ3f79Si+ppESZFqipo0eVZds287mn229XlmHDeMiu\nrVodhn3HHXfg9ttvt+ihDBo0CNOmTcOGDRuaxb/++uuIj4+H0WiETqcDADz77LN4/fXXcerUKQDA\n8uXLsXPnThxpMgHUo48+itLSUhQUFDTb5y233IJ7770XzzzzTLty4zDsa5edDSxbpjz+9a+BjRvV\nzYeos9XWAocOKcXo88+Vc0nWbivRlF5vWZD8/bv3EQNVh2HX1dXhwIEDWNb4zfOLmJgYq4UCAAoL\nCzF69GhT8WmMX7VqFY4fPw5/f38UFhYiJiam2T7T0tLQ0NAAR0fHVhNvT25ERLbS6YARI5QFAH7+\nGfjiC6UgFRUB5eWWAxoA8zmkDz5Qnv/qV+ZidPvtwODBHPbdVIsfRVVVFRoaGuDl5WXRPmDAABiu\ncuWXwWDAjVfMEti4vcFggL+/P4xGY7N9enl5ob6+HlVVVc3WdURua9asMT2Ojo5GdHR0q69B1nXn\nv+iI2svNTRmqHRmpPP/pJ6VXdPAgUFysPK6psdzm3DnLUXY6HXDzzcAttyhLaKh9zfCdk5ODnMZk\nu0CH12Jr52rsQdMCRG3HI5hElnr1UmZYuOMO5fmlS8CXXyrFqHGprrbcprbWvK6Rl5dSiBqLUkiI\nUqjUcOUf51cb/NVRWixAnp6ecHR0hNFotGg3Go3w8fGxuo23t3ezHkjj9t7e3i3GODk5wdPT06bE\n25MbEVFncXY2F5FZs5TDc8eOWRak06ebb2c0mm/UByiH6AYNMveQhg4F/PyUOe56mhYLkFarRVhY\nGDIzM/GHP/zB1J6VlYV7773X6jaRkZFYvnw5amtrTeeBsrKyoNfr4e/vb4p55513LLbLysrCiBEj\nbDr/097cqP04DJuobRwclIlQg4KAXwb34vvvlUN1JSXKUlra/LBdfT1QVqYs6elKW+/eyqG7IUOU\nf2++GfD17f7/F1s9BLdkyRLMmjULERERiIqKwhtvvAGDwYD58+cDAOLj47F//3588sknAID7778f\na9euxezZs5GQkIAjR47ghRdesDgENn/+fLz22mtYvHgx5s2bh927dyMtLQ3btm0zxVy6dAmlpaUA\nlCHZZ86cQXFxMXr37o2bbrrJptyIiOzJDTcAY8cqC6DM1HD0qLkoHT6s9JqudOGCMjx8/35zm4eH\nuRg1Ficvr+5VlGyaDfv111/Hiy++iDNnzuCWW27Byy+/jFGjRgEA5syZg9zcXFRUVJjiDx8+jCee\neAL79u1Dv379MH/+fKxatcpin5999hkWL16M0tJS6PV6LF++HPPmzTOtP3bsGIKCgpQkmwwFjI6O\nRnZ2tk25md4kh2Ffs6wsID5eeTx+PPDCC+rmQ9RTVVcrPaPGolRergxmsEXfvkohCglRRtwNHnxt\nPSXekK4DsABdOxYgInWIKFMGlZcrh+Ua//3xR9u2791buWB20CClIA0apBwW1Gpb35a3YyC7wHNA\nROrQaAAfH2UZN05pE1GuNyorU0beNf7bdObvRhcuKEPFDx40tzk5KTNADB5sWZy6+l5JLEBERN2M\nRqOMjPPzAxqv6b98GTh5UukhlZcDX38NfPWV9cN39fXmaYWa8vJSCtJNNyn/djYWIGoz9oCI7I+D\ngzL1j78/MHGi0iYCfPeduRgdOaL8e/Kk9X00DgnvqjmdWYCIiHoojUbp1Xh5AU3HZv30k9L7OXLE\nXJyOHlUulO1KLEBkE54DIuo5evUy3yepUUMDcPy4+dDc118rk7B2JhYgIiKCo6P5wtnG80qbNnXu\na/bAyR2os7EHREQdgQWIbMLLqIioo7EAERGRKliAyCbsARFRR2MBIiIiVbAAkU04DJuIOhoLEBER\nqYIFiNqMPSAi6ggsQEREpAoWILIJzwERUUdjASIiIlWwAJFN2AMioo7GAkRERKpgASKbcCYEIupo\nLEBERKQKFiBqM54DIqKOwAJENuEhOCLqaCxA1GbsARFRR2ABIiIiVbAAERGRKliAyCa8EJWIOppN\nBSg5ORmBgYFwdXVFeHg48vPzW4wvKSnB2LFj4ebmBj8/P6xfv75ZTG5uLsLCwuDq6org4GCkpKQ0\ni9mxYweGDBkCFxcXDB06FDt37rRYv2bNGjg4OFgsvr6+trwlIiJSWasFKD09HXFxcUhISEBxcTGi\noqIwadIknDx50mr8+fPnMWHCBPj4+KCoqAibN2/Gxo0bkZSUZIqprKzE5MmTMWrUKBQXFyM+Ph4L\nFixARkaGKaawsBCxsbGYNWsWvvjiC8ycORP33nsv9u3bZ/F6ISEhMBgMpqWkpKS9nwW1gD0gIupw\n0oqIiAiZN2+eRdvAgQMlPj7eanxycrJ4eHhITU2NqS0xMVH0er3p+bJly2TQoEEW282dO1ciIyNN\nz6dPny4xMTEWMb/5zW9kxowZpuerV6+W0NDQ1t6C2PA2qRXvvCMSFqYs69apnQ0RdYXO/u5ssQdU\nV1eHAwcOICYmxqI9JiYGBQUFVrcpLCzE6NGjodPpLOJPnz6N48ePm2Ks7bOoqAgNDQ0AgD179tj0\nuhUVFdDr9QgKCsKMGTNQWVnZ0luiduJ1QETU0ZxaWllVVYWGhgZ4eXlZtA8YMAAGg8HqNgaDATfe\neKNFW+P2BoMB/v7+MBqNzfbp5eWF+vp6VFVVwcvLCwaDwWpM09cdOXIk0tLSEBISAqPRiMTERERF\nRaG0tBT9+vWz2HbNmjWmx9HR0YiOjm7prRMRXXdycnKQk5PTZa/XYgFqD00XniCYOHGi6XFoaCgi\nIyMRGBiItLQ0LF682CK2aQGituM5IKKe78o/zteuXdupr9fiIThPT084OjrCaDRatBuNRvj4+Fjd\nxtvbu1nvqHF7b2/vFmOcnJzg6enZYkzjPqxxc3PD0KFDcfTo0ZbeFhER2YEWC5BWq0VYWBgyMzMt\n2rOyshAVFWV1m8jISOTl5aG2ttYiXq/Xw9/f3xSTlZXVbJ8jRoyAo6NjizF33nnnVfOtqalBeXn5\nVYsjdQz2gIioQ7Q2SiE9PV20Wq1s3bpVysrKZOHCheLu7i4nTpwQEZEVK1bI+PHjTfHV1dXi7e0t\nsbGxcvjwYdmxY4f06dNHkpKSTDGVlZXSq1cviYuLk7KyMklNTRWtVisZGRmmmIKCAnFycpLnn39e\nysvLZcOGDeLs7Cz79u0zxTz11FOSm5srFRUVsmfPHpkyZYp4eHiYcmtkw9ukVuzYYR4Fl5iodjZE\n1BU6+7vTpr0nJydLQECA6HQ6CQ8Pl7y8PNO62bNnS2BgoEV8SUmJjBkzRlxcXMTX11fWWRm3m5ub\nK8OHDxedTidBQUGSkpLSLObtt9+WkJAQ0Wq1MmTIEHnnnXcs1sfGxoqvr69otVrR6/Uybdo0KS8v\nb/4mWYCuWdMC9OyzamdDRF2hs787Nb+8SI+m0WhwHbzNTrVjB/Dcc8rj3/8eWLlS3XyIqPN19ncn\n54KjNuM5ICLqCCxAZBN2IImoo7EAERGRKliAyCbsARFRR2MBIiIiVbAAkU04FQ8RdTQWICIiUgUL\nELUZe0BE1BFYgIiISBUsQGQTngMioo7GAkRERKpgAaI2Yw+IiDoCCxDZhBeiElFHYwEiIiJVsACR\nTdgDIqKOxgJEbcZzQETUEViAyCbsARFRR2MBIiIiVbAAUZvxEBwRdQQWICIiUgULENmEU/EQUUdj\nASIiIlWwAJFN2AMioo7GAkRERKpgASIiIlWwAJFNeCEqEXU0mwpQcnIyAgMD4erqivDwcOTn57cY\nX1JSgrFjx8LNzQ1+fn5Yv359s5jc3FyEhYXB1dUVwcHBSElJaRazY8cODBkyBC4uLhg6dCh27tx5\nzbnZi5ycHLVTsMqWvLr6HJA9flbMyTb2mBNgn3nZY06drdUClJ6ejri4OCQkJKC4uBhRUVGYNGkS\nTp48aTX+/PnzmDBhAnx8fFBUVITNmzdj48aNSEpKMsVUVlZi8uTJGDVqFIqLixEfH48FCxYgIyPD\nFFNYWIjY2FjMmjULX3zxBWbOnIl7770X+/bta3du9sRef9mulpeaPSB7/KyYk23sMSfAPvOyx5w6\nW6sFKCkpCXPmzMEjjzyCwYMH45VXXoGPjw9ef/11q/H/+7//i5qaGqSlpWHIkCH4wx/+gOXLl1sU\noDfeeAN+fn7YvHkzBg8ejLlz5+Khhx7CSy+9ZIrZtGkTxo0bh/j4eAwePBgrV65EdHQ0Nm3a1O7c\nqGNwFBwRdQSnllbW1dXhwIEDWLZsmUV7TEwMCgoKrG5TWFiI0aNHQ6fTWcSvWrUKx48fh7+/PwoL\nCxETE9Nsn2lpaWhoaICjoyP27NmDhQsXNovZsmVLu3JbvLild9r19uwBqqvVzqK5q+XVDTqVRNTd\nSAu+/fZb0Wg0kpeXZ9G+du1aGTx4sNVtJkyYII888ohF2/Hjx0Wj0ciePXtERGTQoEGyfv16i5jc\n3FzRaDRiMBhERESr1cpbb71lEZOWliY6na7NuQHgwoULFy7tWDpTiz2g9tDY4fEZ4RAuIiK70+I5\nIE9PTzg6OsJoNFq0G41G+Pj4WN3G29sbBoOhWXzjupZinJyc4Onp2WJM4z7akxsREdmPFguQVqtF\nWFgYMjMzLdqzsrIQFRVldZvIyEjk5eWhtrbWIl6v18Pf398Uk5WV1WyfI0aMgKOjY4sxd955Z7tz\nIyIiO9LaMbr09HTRarWydetWKSsrk4ULF4q7u7ucOHFCRERWrFgh48ePN8VXV1eLt7e3xMbGyuHD\nh2XHjh3Sp08fSUpKMsVUVlZKr169JC4uTsrKyiQ1NVW0Wq1kZGSYYgoKCsTJyUmef/55KS8vlw0b\nNoizs7Ps27fP5tyIiMh+2XSGKTk5WQICAkSn00l4eLjFif/Zs2dLYGCgRXxJSYmMGTNGXFxcxNfX\nV9atW9dsn7m5uTJ8+HDR6XQSFBQkKSkpzWLefvttCQkJEa1WK0OGDJF33nnHYvu7775bPDw8BIA4\nOzs3y2316tXi6+srrq6uEh0dLaWlpRb7r6mpkSeffFI8PT2lV69eMnXqVDl16pQtH4lVGzZskPDw\ncOnTp4/0799f7r77bjl8+HCzuK7M67XXXpNbb71V+vTpI3369JHIyEj54IMPVMvHmg0bNohGo5En\nn3xStbxWr14tGo3GYvHx8VEtn0anT5+WBx98UPr37y8uLi4yZMgQyc3NVTUvf3//Zp+VRqORKVOm\niIjI5cuXuzynS5cuSXx8vAQGBoqLi4sEBgZKQkKC1NfXW8R1dV7nz5+XRYsWib+/v7i6ukpUVJTs\n37+/y3Jq/J7U6/Wi0WjkzTffbBbTEa//ww8/yAMPPCAeHh7i4eEhs2bNknPnzrWaX+cOcehEH374\noTz99NPy9ttvi5ubm6SlpVmsf/7558Xd3V0yMjLk8OHDMn36dPH19ZUff/zRFDN//nzx9fWVTz75\nRA4cOCDR0dFy++23S0NDQ7tyuuuuu+TNN9+U0tJSKSkpkXvuuUe8vb3lhx9+UC2vd999V/7zn//I\nN998I19//bU8/fTT4uzsLMXFxap9Tk0VFhZKYGCg3HbbbbJgwQJTe1fntXr1arn55pvFaDSalqqq\nKtXyERE5e/asBAYGykMPPST79++XY8eOSXZ2tpSXl6uaV1VVlcXndPDgQXFwcJC///3vquW0du1a\n6devn7z//vty/Phxee+996Rfv34Wo23VyGv69OmmPxq++eYbWbNmjXh4eMi3337bJTl11ffkxIkT\nJTQ0VPbs2SOFhYUydOhQufvuu1vNr9sWoKZ69+5t8cFevnxZvL29ZcOGDaa2ixcviru7u6mnde7c\nOdFqtfLPf/7TFHPy5ElxcHCQjz/+uEPyunDhgjg6Osr7779vV3n169dP/vKXv6iez7lz5yQ4OFhy\ncnIkOjraVIDUyGv16tUSGhpqdZ1an1N8fLyMGjXqquvV/vk1SkxMlL59+0pNTY1qOf32t7+V2bNn\nW7Q9+OCD8tvf/lZE1Pmsfv75Z3FycpL33nvPoj0sLEwSEhJERLo0p876niwrKxONRiMFBQWmmPz8\nfNFoNHLkyJEWc+qRk5FWVlbCaDRaXOzq4uKCMWPGmC5S/fzzz3Hp0iWLGD8/P9x8881Xvci2rc6f\nP4/Lly+jb9++dpFXQ0MDtm3bhpqaGowZM0b1fObNm4d7770XY8eOtRgqr1ZeFRUV0Ov1CAoKwowZ\nM1BZWalqPjt37kRERATuu+8+eHl5YdiwYaYLsdXMqykRwV//+lc88MAD0Ol0quU0adIkZGdn48iR\nIwCAsrIy7Nq1C1OmTAGgzmdVX1+PhoYGi4vyG1939+7dqv/8rvX1CwsLASiTD/Tu3RuRkZGmmKio\nKPTq1csUczU9sgA1Dt/28vKyaB8wYIBpncFggKOjI2644QaLGC8vr2ZDu9tr0aJFGDZsmOkHo1Ze\nJSUl6N27N1xcXDBv3jxs374dgwcPVvVzSk1NRUVFBRITEwFYXj+mRl4jR45EWloaPv74Y6SmpsJg\nMCAqKgo//PCDap9TRUUFkpOTcdNNNyEzMxOLFi3CihUrTEXIHn7Ps7KycOzYMTz66KOq5vT4449j\n5syZuPnmm6HVahEaGorZs2dj/vz5quXl7u6OyMhIJCYm4vTp02hoaMA//vEP7NmzB2fOnFH953et\nr980pn///hbrNRqNxX6upsMvRLV3XXWh7JIlS1BQUID8/HybXrMz8woJCcGhQ4dQXV2Nf/3rX4iN\njcWuXbtUy+fIkSN4+umnkZ+fbxp2L8rh4Fa37ay8Jk6caHocGhqKyMhIBAYGIi0tDXfccUeX5wMA\nly9fRkREBJ599lkAwG233Yavv/4aW7ZswRNPPNHitl31e56amoqIiAjccsstrcZ2Zk6vvPIK/va3\nv2Hbtm0YOnQoDh48iEWLFiEgIAAPP/ywanm99dZbePjhh+Hn5wdHR0eEhYVhxowZ+Pzzz1XLyRat\nvb4t/1dt0SN7QI0Xq1q7SLXpxbANDQ34/vvvLWIMBoMppr0WL16M9PR0ZGdnIyAgQPW8nJ2dERQU\nhGHDhmHDhg0YOXIktmzZYrpgt6vzKSwsRFVVFYYOHQpnZ2c4Ozvjs88+Q3JyMrRareliZLV+fgDg\n5uaGoUOH4ujRo6p9Tr6+vhgyZIhFW0hICE6cOGF6TTXyavTdd9/hvffeM/V+1Mzp2WefxcqVKzF9\n+nQMHToUDzzwAJYsWYLnnntO1byCgoKQk5ODn376CadOncKePXtQV1eH4OBg1X9+1/L6V8b83//9\nn8V6EcF3333Xao49sgAFBgbC29vb4iLVmpoa5Ofnmy5SDQsLg7Ozs0XMqVOn8OWXX17ThayLFi0y\nFZ9BgwbZTV5NNTQ04PLly6rlc8899+Dw4cP44osv8MUXX6C4uBjh4eGYMWMGiouLMXDgQNU/p5qa\nGpSXl8PHx0e1z+nOO+/El19+adH21Vdfmf6oUfv36c0334SLiwtmzJhhalMrJxGBg4Pl15mDg4Pp\nL3W1PytXV1d4eXnh7NmzyMzMxO9+9zvVc+qo14+MjMSFCxcszvcUFhbip59+aj3HNg2jsCMXLlyQ\ngwcPysGDB8XNzU3WrVsnBw8eNF2E+sILL4iHh4dkZGRISUmJ3HfffaLX6+XChQumffzxj38UPz8/\ni+GFw4YNk8uXL7crp8cff1z69Okj2dnZcubMGdPS9DW7Oq/ly5dLXl6eVFZWyqFDh2TFihXi4OAg\nmZmZqn1O1owdO9biOqCuzuupp56S3NxcqaiokD179siUKVPEw8ND1d+n/fv3i7Ozszz77LPy9ddf\ny/bt28XDw0OSk5NV+5waXb58WQYOHCjz5s1rtk6NnB599FHx8/OTDz74QCorKyUjI0P69+8vS5cu\nVTWvjz/+WD788EOpqKiQzMxMue222yQyMtJ0fVJn59RV35OTJk2SW265RQoLC6WgoEBCQ0Nl6tSp\nrebXbQvQrl27TBfAOTg4mB7PmTPHFLNmzRrx8fERFxcXqxdY1dbWyoIFC+SGG24QNze3a77o7Mpc\nGpe1a9daxHVlXrNnzxZ/f3/R6XQyYMAAmTBhgqn4qJHP1TQdhq1GXrGxseLr6ytarVb0er1MmzbN\n4nqbrs6n0QcffCC33XabuLi4yODBg+XVV19tFqNGXtnZ2eLg4NDsokq1crpw4YI89dRTEhAQIK6u\nrhIUFCRPP/201NbWqprX9u3bJTg4WHQ6nfj4+MiCBQvk/PnzXZZTV31Pnj17Vh544AHTBe+zZs2S\n6urqVvPTiHCqaCIi6no98hwQERHZPxYgIiJSBQsQERGpggWIiIhUwQJERESqYAEiIiJV/H//1nLt\nlB4dwwAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now assume we're spotting a few more trains, and let's see how our posterior updates as we measure this data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for train in [782, 423]:\n", " suite.Update(train)\n", "\n", "print 'Given the data, our mean estimate is:',\n", "print suite.Mean()\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Given the data, our mean estimate is: 877.54322135\n" ] }, { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEACAYAAAB/BTv2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 35 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Euro problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Estimation:* based on the data (140h, 110t), what is the probability that the coin lands heads?\n", " \n", "*Hypothesis testing:* what is the probability that the coin is fair?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Euro(thinkbayes.Suite):\n", "\n", " def Likelihood(self, hypo, data):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " hypo: integer value of x, the probability of heads (0-100)\n", " data: string 'H' or 'T'\n", " \"\"\"\n", " # Note: while we're writing the likelihood function, we\n", " # *assume* the hypothesis is true. Don't forget this!\n", " \n", " # We convert the hypothesis to a probability in the 0-1 range:\n", " x = hypo/100.0\n", " \n", " # Now, since the hypothesis is about Heads, if the data is H\n", " # it means the hypothesis is True, so we can just return the hypothesis\n", " if data == 'H':\n", " return x\n", " else:\n", " return 1-x\n", "\n", "suite = Euro(range(0, 101))\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEACAYAAACH5cABAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9MVFf+PvBn+DHMoEBqgZkBIoygVUiqFsSAFUitGFt/\nxHRVtFrRVOPaotY2RpRNBfzRpimpNo4lENv9Y400gm5TmygtgrBg1RUMCFhTKWh0JmG34tquiPj+\n/NHl6nXGYfSLV7/wvBISOPd9zj3nxN6HO8yd6kREQERE9IR5Pe0JEBHR0MDAISIiTTBwiIhIEwwc\nIiLSBAOHiIg0wcAhIiJNeBQ4NpsNVqsVRqMRCQkJqKmpcVvf2NiI1NRU+Pv7IyIiAvn5+arjdrsd\nixcvxrhx4+Dj44Ply5e7HKe0tBSxsbEwGAyIi4vD4cOHH3rOnTt3wsvLC1lZWZ4siYiINNZv4JSU\nlGD9+vXIyclBQ0MDkpOTMXPmTFy+fNll/Y0bNzB9+nRYLBacOXMGu3btwieffIKCggKlpru7GyEh\nIcjOzsbkyZOh0+mcxqmrq0NGRgaWLl2Kc+fO4c0338T8+fNx6tQpp9qTJ0+iqKgIL774osuxiIjo\nGSD9SExMlFWrVqnaRo8eLdnZ2S7rbTabBAUFya1bt5S2bdu2SXh4uMv6WbNmyfLly53aFyxYIOnp\n6aq2V199VRYtWqRqu379ukRHR0tlZaWkpaVJVlZWf0siIqKnwO0dzu3bt3H27Fmkp6er2tPT01Fb\nW+uyT11dHaZOnQo/Pz9V/dWrV9He3u5xEJ48edKj865atQrz589HamoqhB+aQET0zPJxd7CzsxO9\nvb0wmUyq9tDQUNjtdpd97HY7Ro4cqWrr62+32xEZGenRxOx2u9N5TSaT6rxFRUW4dOkS9u/fDwAP\nfTmNL7MRET2egfxFfsDfpabVxf3ChQvYsmUL/va3v8Hb2xvAHxvzsM3pOzbUvz788MOnPodn5Yt7\nwb3gXrj/Gmhu73CCg4Ph7e0Nh8Ohanc4HLBYLC77mM1mp7ufvv5ms9njiT1snL7z1tXVobOzE3Fx\nccrx3t5eVFdXo7CwEL/99ht8fX09Ph8RET1Zbu9w9Ho94uPjcezYMVV7eXk5kpOTXfZJSkpCdXU1\nuru7VfXh4eEev5zWN055eflDzztv3jw0NTXh3LlzOHfuHBoaGpCQkIBFixahoaGBYUNE9Ixxe4cD\nABs2bMDSpUuRmJiI5ORkfPHFF7Db7Vi9ejUAIDs7G6dPn8b3338PAFi8eDFyc3ORmZmJnJwcXLhw\nAR9//DG2bt2qGrehoQEA0NXVBS8vLzQ0NECv1yM2NhYAsG7dOqSkpODjjz/G3LlzcejQIVRWVuIf\n//gHACAoKAhBQUGqMf39/fHcc88pY5CztLS0pz2FZwb34h7uxT3ciydIPGCz2SQqKkr8/PwkISFB\nqqurlWOZmZlitVpV9Y2NjZKSkiIGg0HCwsIkLy/PaUydTic6nU68vLyU7x8c5+DBgzJ27FjR6/US\nGxsrhw4dcjvPh70t2sNlEhHRfQb62qn736CDmk6neyJ/ACMiGswG+trJz1IjIiJNMHCIiEgTDBwi\nItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAh\nIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEwwc\nIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHA\nISIiTXgUODabDVarFUajEQkJCaipqXFb39jYiNTUVPj7+yMiIgL5+fmq43a7HYsXL8a4cePg4+OD\n5cuXuxyntLQUsbGxMBgMiIuLw+HDh1XHd+7ciUmTJiEoKAihoaGYM2cOzp8/78mSiIhIY/0GTklJ\nCdavX4+cnBw0NDQgOTkZM2fOxOXLl13W37hxA9OnT4fFYsGZM2ewa9cufPLJJygoKFBquru7ERIS\nguzsbEyePBk6nc5pnLq6OmRkZGDp0qU4d+4c3nzzTcyfPx+nTp1SaqqqqvDuu++irq4OFRUV8PHx\nwauvvopff/31cfaCiIieIJ2IiLuCyZMnY8KECSgsLFTaxowZgz/96U/YsWOHU/3evXuRnZ0Nh8MB\nPz8/AMD27duxd+9eXLlyxal+9uzZCAkJwb59+1TtCxcuxPXr13H06FGlbfr06QgJCcH+/ftdzvW3\n335DUFAQ/v73v+P111+/t0idDv0sk4iIHjDQ1063dzi3b9/G2bNnkZ6ermpPT09HbW2tyz51dXWY\nOnWqEjZ99VevXkV7e7vHEzt58uQjnRf44+7q7t27eO655zw+DxERacPH3cHOzk709vbCZDKp2kND\nQ2G32132sdvtGDlypKqtr7/dbkdkZKRHE7Pb7U7nNZlMDz0vAKxbtw4TJ05EUlKS07GtW7cq36el\npSEtLc2jeRARDRWVlZWorKx8YuO7DZzH4ervMVrYsGEDamtrUVNT43IO9wcOERE5e/CX8dzc3AEd\n323gBAcHw9vbGw6HQ9XucDhgsVhc9jGbzU53IX39zWazxxN72Diuxnjvvffw9ddf4/jx44iKivL4\nHEREpB23f8PR6/WIj4/HsWPHVO3l5eVITk522ScpKQnV1dXo7u5W1YeHh3v8clrfOOXl5U7nnTJl\niqpt3bp1KCkpQUVFBcaMGePx+EREpDHpR0lJiej1eikuLpbm5mZZu3atBAQESEdHh4iIbNq0SaZN\nm6bUd3V1idlsloyMDGlqapLS0lIJDAyUgoIC1bj19fVSX18vU6dOlTlz5kh9fb2cP39eOV5bWys+\nPj7y0UcfSUtLi+zYsUN8fX3l1KlTSs2aNWskMDBQKioq5Nq1a8rXzZs3VefyYJlERPSAgb52ejSa\nzWaTqKgo8fPzk4SEBKmurlaOZWZmitVqVdU3NjZKSkqKGAwGCQsLk7y8PKcxdTqd6HQ68fLyUr5/\ncJyDBw/K2LFjRa/XS2xsrBw6dMhpjPv7933l5uaqF8nAISJ6ZAN97ez3OZzBgM/hEBE9Ok2fwyEi\nIhooDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEwwcIiLSBAOH\niIg0wcAhIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBw\niIhIEwwcIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQD\nh4iINMHAISIiTTBwiIhIEwwcIiLShEeBY7PZYLVaYTQakZCQgJqaGrf1jY2NSE1Nhb+/PyIiIpCf\nn686brfbsXjxYowbNw4+Pj5Yvny5y3FKS0sRGxsLg8GAuLg4HD58+P95bkRE9HT0GzglJSVYv349\ncnJy0NDQgOTkZMycOROXL192WX/jxg1Mnz4dFosFZ86cwa5du/DJJ5+goKBAqenu7kZISAiys7Mx\nefJk6HQ6p3Hq6uqQkZGBpUuX4ty5c3jzzTcxf/58nDp16rHnRkRET5H0IzExUVatWqVqGz16tGRn\nZ7ust9lsEhQUJLdu3VLatm3bJuHh4S7rZ82aJcuXL3dqX7BggaSnp6vaXn31VVm0aNEjz82DZRIR\n0QMG+trp9g7n9u3bOHv2LNLT01Xt6enpqK2tddmnrq4OU6dOhZ+fn6r+6tWraG9v9zgIT5486fa8\njzM3IiJ6enzcHezs7ERvby9MJpOqPTQ0FHa73WUfu92OkSNHqtr6+tvtdkRGRno0Mbvd7nRek8mk\nnPdR57Z161bl+7S0NKSlpXk0DyKioaKyshKVlZVPbHy3gfM4XP095llwf+AQEZGzB38Zz83NHdDx\n3b6kFhwcDG9vbzgcDlW7w+GAxWJx2cdsNjvdYfT1N5vNHk/sYeP0jfE4cyMioqfHbeDo9XrEx8fj\n2LFjqvby8nIkJye77JOUlITq6mp0d3er6sPDwz1+Oa1vnPLycqfzTpky5bHnRkRET1F/7yooKSkR\nvV4vxcXF0tzcLGvXrpWAgADp6OgQEZFNmzbJtGnTlPquri4xm82SkZEhTU1NUlpaKoGBgVJQUKAa\nt76+Xurr62Xq1KkyZ84cqa+vl/PnzyvHa2trxcfHRz766CNpaWmRHTt2iK+vr5w6dcrjufXxYJlE\nRPSAgb52ejSazWaTqKgo8fPzk4SEBKmurlaOZWZmitVqVdU3NjZKSkqKGAwGCQsLk7y8PKcxdTqd\n6HQ68fLyUr5/cJyDBw/K2LFjRa/XS2xsrBw6dOiR5qYskoFDRPTIBvraqfvfoIOaTqfDEFgmEdGA\nGuhrJz9LjYiINMHAISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwiItIEA4eIiDTB\nwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTfg87QloJSHhac+AiGho4x0OERFpgoFDRESa4P/x\nk4iIXOL/8ZOIiP6/xMAhIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iI\nNMHAISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwiItKER4Fjs9lgtVphNBqRkJCA\nmpoat/WNjY1ITU2Fv78/IiIikJ+f71RTVVWF+Ph4GI1GREdHo7CwUHW8p6cHeXl5iImJgdFoxIQJ\nE3D06FFVzZ07d7B582aMGjUKRqMRo0aNwl/+8hf09vZ6siwiItKS9OPAgQPi6+srxcXF0traKllZ\nWTJ8+HDp6OhwWd/V1SUmk0kWLlwo58+fl4MHD0pAQIB8+umnSs2lS5fE399f1q5dK62trVJUVCS+\nvr5SWlqq1GzcuFEsFot899130tbWJnv37hWj0Sj19fVKTW5urowYMUK+/fZbaW9vl2+++UZGjBgh\n+fn5qjl5sEwiInrAQF87+x0tMTFRVq1apWobPXq0ZGdnu6y32WwSFBQkt27dUtq2bdsm4eHhys8b\nN26UMWPGqPq9/fbbkpSUpPxssVhk9+7dqpo33nhDlixZovw8a9YsyczMVNW89dZbMnv2bFUbA4eI\n6NEN9LXT7Utqt2/fxtmzZ5Genq5qT09PR21trcs+dXV1mDp1Kvz8/FT1V69eRXt7u1LjaswzZ84o\nL4fdvn1bNQYAGAwG1ct5M2fOREVFBS5cuAAAaG5uxvHjx/Haa6+5WxYRET0FPu4OdnZ2ore3FyaT\nSdUeGhoKu93uso/dbsfIkSNVbX397XY7IiMj4XA4nMY0mUy4c+cOOjs7YTKZMGPGDHz22WdIS0tD\nTEwMfvjhB5SVleGP0P3DmjVrcOXKFYwbNw4+Pj64c+cOcnJysHr1aqd5bd26Vfk+LS0NaWlp7pZO\nRDTkVFZWorKy8omN7zZwHodOpxuQcXbt2oWVK1ciNjYWOp0OMTExWLFiBfbt26fU7N69G19++SUO\nHDiAuLg41NfXY926dYiKisKKFStU490fOERE5OzBX8Zzc3MHdHy3L6kFBwfD29sbDodD1e5wOGCx\nWFz2MZvNTnc/ff3NZrPbGh8fHwQHByvnPnToEH7//Xd0dHSgpaUFw4YNQ3R0tNJn+/bt2Lx5MxYs\nWIC4uDgsWbIEGzZswM6dOz1ZOxERacht4Oj1esTHx+PYsWOq9vLyciQnJ7vsk5SUhOrqanR3d6vq\nw8PDERkZqdSUl5c7jTlp0iR4e3s7zcFisaCnpwelpaWYO3euckxE4OWlXoKXl5fqZTciInpG9Peu\ngpKSEtHr9VJcXCzNzc2ydu1aCQgIUN4WvWnTJpk2bZpS39XVJWazWTIyMqSpqUlKS0slMDBQCgoK\nlJq2tjYZNmyYrF+/Xpqbm6WoqEj0er2UlZUpNT/++KOUlpbKzz//LCdOnJBXXnlFoqOjpaurS6lZ\nuXKlREREyJEjR6StrU3KysokJCREPvjgA9UaPFgmERE9YKCvnR6NZrPZJCoqSvz8/CQhIUGqq6uV\nY5mZmWK1WlX1jY2NkpKSIgaDQcLCwiQvL89pzKqqKnnppZfEz89PRo0aJYWFhU7HY2NjxWAwSHBw\nsCxbtkyuXbumqrl586a8//77EhUVJUajUUaNGiVbtmyR7u5u9SIZOEREj2ygr526/w06qOl0Or7M\nRkT0iAb62snPUiMiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAh\nIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEwwc\nIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHA\nISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAhIiJNeBQ4NpsNVqsVRqMRCQkJqKmpcVvf2NiI1NRU+Pv7\nIyIiAvn5+U41VVVViI+Ph9FoRHR0NAoLC1XHe3p6kJeXh5iYGBiNRkyYMAFHjx51GufatWtYtmwZ\nQkNDYTQaERcXhxMnTniyLCIi0pL048CBA+Lr6yvFxcXS2toqWVlZMnz4cOno6HBZ39XVJSaTSRYu\nXCjnz5+XgwcPSkBAgHz66adKzaVLl8Tf31/Wrl0rra2tUlRUJL6+vlJaWqrUbNy4USwWi3z33XfS\n1tYme/fuFaPRKPX19UrNr7/+KlarVZYtWyanT5+WX375RSoqKqSlpUU1Jw+WSUREDxjoa2e/oyUm\nJsqqVatUbaNHj5bs7GyX9TabTYKCguTWrVtK27Zt2yQ8PFz5eePGjTJmzBhVv7fffluSkpKUny0W\ni+zevVtV88Ybb8iSJUuUn7Ozs+Xll1/ubwkMHCKixzDQ1063L6ndvn0bZ8+eRXp6uqo9PT0dtbW1\nLvvU1dVh6tSp8PPzU9VfvXoV7e3tSo2rMc+cOYPe3l7l3PePAQAGg0H1ct7hw4eRmJiIhQsXwmQy\nYeLEidizZ4+7JRER0VPi4+5gZ2cnent7YTKZVO2hoaGw2+0u+9jtdowcOVLV1tffbrcjMjISDofD\naUyTyYQ7d+6gs7MTJpMJM2bMwGeffYa0tDTExMTghx9+QFlZGf4I3T9cunQJNpsNGzZswObNm1Ff\nX4+srCwAwDvvvKMaf+vWrcr3aWlpSEtLc7d0IqIhp7KyEpWVlU9sfLeB8zh0Ot2AjLNr1y6sXLkS\nsbGx0Ol0iImJwYoVK7Bv3z6l5u7du0hMTMT27dsBAOPHj8fFixexZ88et4FDRETOHvxlPDc3d0DH\nd/uSWnBwMLy9veFwOFTtDocDFovFZR+z2ex099PX32w2u63x8fFBcHCwcu5Dhw7h999/R0dHB1pa\nWjBs2DBER0crfcLCwhAbG6saZ+zYsejo6HC3LCIiegrcBo5er0d8fDyOHTumai8vL0dycrLLPklJ\nSaiurkZ3d7eqPjw8HJGRkUpNeXm505iTJk2Ct7e30xwsFgt6enpQWlqKuXPnKsemTJmC1tZWVf1P\nP/2EqKgod8siIqKnob93FZSUlIher5fi4mJpbm6WtWvXSkBAgPK26E2bNsm0adOU+q6uLjGbzZKR\nkSFNTU1SWloqgYGBUlBQoNS0tbXJsGHDZP369dLc3CxFRUWi1+ulrKxMqfnxxx+ltLRUfv75Zzlx\n4oS88sorEh0dLV1dXUrN6dOnxdfXV7Zv3y4XL16Ur7/+WoKCgsRms6nW4MEyiYjoAQN97fRoNJvN\nJlFRUeLn5ycJCQlSXV2tHMvMzBSr1aqqb2xslJSUFDEYDBIWFiZ5eXlOY1ZVVclLL70kfn5+MmrU\nKCksLHQ6HhsbKwaDQYKDg2XZsmVy7do1p3GOHDki48ePF4PBIC+88IJ8/vnnzotk4BARPbKBvnbq\n/jfooKbT6TAElklENKAG+trJz1IjIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi\n0gQDh4iINMHAISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwiItIEA4eIiDTBwCEi\nIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEwwcIiLSBAOHiIg0wcAhIiJNMHCIiEgTDBwi\nItIEA4eIiDTBwCEiIk0wcIiISBMMHCIi0gQDh4iINMHAISIiTTBwiIhIEx4Fjs1mg9VqhdFoREJC\nAmpqatzWNzY2IjU1Ff7+/oiIiEB+fr5TTVVVFeLj42E0GhEdHY3CwkLV8Z6eHuTl5SEmJgZGoxET\nJkzA0aNHH3rOnTt3wsvLC1lZWZ4saciqrKx82lN4ZnAv7uFe3MO9eHL6DZySkhKsX78eOTk5aGho\nQHJyMmbOnInLly+7rL9x4wamT58Oi8WCM2fOYNeuXfjkk09QUFCg1LS1teG1117Dyy+/jIaGBmRn\nZyMrKwtlZWVKTU5ODr744gt8/vnnaGlpwerVqzFv3jw0NDQ4nfPkyZMoKirCiy++CJ1O9zj7MGTw\nP6Z7uBf3cC/u4V48QdKPxMREWbVqlapt9OjRkp2d7bLeZrNJUFCQ3Lp1S2nbtm2bhIeHKz9v3LhR\nxowZo+r39ttvS1JSkvKzxWKR3bt3q2reeOMNWbJkiart+vXrEh0dLZWVlZKWliZZWVlOc/JgmUPG\nhx9++LSn8MzgXtzDvbiHe3HPQF873d7h3L59G2fPnkV6erqqPT09HbW1tS771NXVYerUqfDz81PV\nX716Fe3t7UqNqzHPnDmD3t5e5dz3jwEABoPB6eW8VatWYf78+UhNTcUf+0NERM8iH3cHOzs70dvb\nC5PJpGoPDQ2F3W532cdut2PkyJGqtr7+drsdkZGRcDgcTmOaTCbcuXMHnZ2dMJlMmDFjBj777DOk\npaUhJiYGP/zwA8rKylShUlRUhEuXLmH//v0A4PblNL7Udk9ubu7TnsIzg3txD/fiHu7Fk+E2cB7H\nQF3Yd+3ahZUrVyI2NhY6nQ4xMTFYsWIF9u3bBwC4cOECtmzZgpqaGnh7ewMARMTlXQ7vfIiInj63\nL6kFBwfD29sbDodD1e5wOGCxWFz2MZvNTnc/ff3NZrPbGh8fHwQHByvnPnToEH7//Xd0dHSgpaUF\nw4YNQ3R0NIA/Xpbr7OxEXFwcfH194evrixMnTsBms0Gv16Onp8fTPSAiIg24DRy9Xo/4+HgcO3ZM\n1V5eXo7k5GSXfZKSklBdXY3u7m5VfXh4OCIjI5Wa8vJypzEnTZqk3K3cPweLxYKenh6UlpZi7ty5\nAIB58+ahqakJ586dw7lz59DQ0ICEhAQsWrQIDQ0N8PX19XALiIhIE/29q6CkpET0er0UFxdLc3Oz\nrF27VgICAqSjo0NERDZt2iTTpk1T6ru6usRsNktGRoY0NTVJaWmpBAYGSkFBgVLT1tYmw4YNk/Xr\n10tzc7MUFRWJXq+XsrIypebHH3+U0tJS+fnnn+XEiRPyyiuvSHR0tHR1dT10rqmpqfLuu+8+6hsn\niIhIAx69581ms0lUVJT4+flJQkKCVFdXK8cyMzPFarWq6hsbGyUlJUUMBoOEhYVJXl6e05hVVVXy\n0ksviZ+fn4waNUoKCwudjsfGxorBYJDg4GBZtmyZXLt2ze08Xb0tes+ePRIVFSUGg0Hi4+NVcx+s\nduzYIQkJCRIYGCghISEye/ZsaWpqcqr78MMPJSwsTIxGo6Slpcn58+efwmy1tWPHDtHpdE6/mAyV\nvbh69aq89dZbEhISIgaDQWJjY6WqqkpVMxT2oqenR7Kzs8VqtYrBYBCr1So5OTly584dVd1g3Iuq\nqiqZPXu2hIeHi06nk6+++sqppr9137p1S959910JDg6WYcOGyZw5c+TKlSv9nntQP6By4MAB8fX1\nleLiYmltbZWsrCwZPny4cnc2WM2YMUO++uorOX/+vDQ2Nsq8efPEbDbLv//9b6Xmo48+koCAACkr\nK5OmpiZZsGCBhIWFyX/+85+nOPMnq66uTqxWq4wfP171i8lQ2Ytff/1VrFarLFu2TE6fPi2//PKL\nVFRUSEtLi1IzVPYiNzdXRowYId9++620t7fLN998IyNGjJD8/HylZrDuxXfffSdbtmyRgwcPir+/\nv/z1r39VHfdk3atXr5awsDD5/vvv5ezZs5KWliYTJkyQ3t5et+ce1IHzqA+tDlY3b94Ub29v+fbb\nb0VE5O7du2I2m2XHjh1KzX//+18JCAhwutMcLB72gPBQ2ovs7Gx5+eWXH3p8KO3FrFmzJDMzU9X2\n1ltvyaxZs0Rk6OzF8OHDVYHjybqvX78uer1e9u/fr9RcvnxZvLy85OjRo27PN2g/vPNxHlodrG7c\nuIG7d+/iueeeA/DHRws5HA7V3hgMBqSkpAzavXnYA8JDaS8OHz6MxMRELFy4ECaTCRMnTsSePXuU\n40NpL2bOnImKigpcuHABANDc3Izjx4/j9ddfBzC09uJ+nqz7n//8J3p6elQ1ERERGDduXL97M+DP\n4TwrHueh1cFq3bp1mDhxIpKSkgBAWb+rvbl69arm83vS3D0gPJT24tKlS7DZbNiwYQM2b96M+vp6\n5cNu33nnnSG1F2vWrMGVK1cwbtw4+Pj44M6dO8jJycHq1asBDK1/F/fzZN12ux3e3t54/vnnVTUm\nk8npEZoHDdrAoT9s2LABtbW1qKmp8eih3MH2iQyP8oDwgwbbXty9exeJiYnYvn07AGD8+PG4ePEi\n9uzZg3feecdt38G2F7t378aXX36JAwcOIC4uDvX19Vi3bh2ioqKwYsUKt30H2154aiDWPWhfUnuc\nh1YHm/feew8lJSWoqKhAVFSU0t73AK6rvek7Nlj094Bw34PGQ2EvwsLCEBsbq2obO3YsOjo6AAyt\nfxfbt2/H5s2bsWDBAsTFxWHJkiXYsGEDdu7cCWBo7cX9PFm32WxGb28v/vWvf6lq7HZ7v3szaAPn\ncR5aHUyxAxz8AAABx0lEQVTWrVunhM2YMWNUx6xWK8xms2pvbt26hZqamkG3N/09IDx69OghsxdT\npkxBa2urqu2nn35SfhkZSv8uRAReXurLn5eXl3LnO5T24n6erDs+Ph6+vr6qmitXrqC1tbX/vRm4\n9zs8e/p7aHWwWrNmjQQGBkpFRYVcu3ZN+bp586ZS8/HHH0tQUJCUlZVJY2OjLFy4UMLDw1U1g9WD\nDwgPlb04ffq0+Pr6yvbt2+XixYvy9ddfS1BQkNhsNqVmqOzFypUrJSIiQo4cOSJtbW1SVlYmISEh\n8sEHHyg1g3Uvbt68KfX19VJfXy/+/v6Sl5cn9fX1ynXRk3X/+c9/loiICNXboidOnCh37951e+5B\nHTgi7h9aHax0Op14eXmJTqdTfeXm5qrqtm7dKhaLRQwGw6B5qM0Trh4QHip7ceTIERk/frwYDAZ5\n4YUX5PPPP3eqGQp7cfPmTXn//fclKipKjEajjBo1SrZs2SLd3d2qusG4F8ePH1euCfdfJ5YvX67U\n9Lfu7u5uycrKkueff178/f09fvBTJ8KPUiYioidv0P4Nh4iIni0MHCIi0gQDh4iINMHAISIiTTBw\niIhIEwwcIiLSxP8BDBxFmwOTIEAAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "suite.Update('H')\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEACAYAAAB27puMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbywmRER03/4fu60Du9ak7yQAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "suite.Update('H')\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEACAYAAAB27puMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "suite.Update('T')\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEACAYAAAB27puMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "for outocme in 'HHHHHHHHTTT':\n", " suite.Update(outocme)\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAEACAYAAAB27puMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "suite = Euro(range(0, 101))\n", "for outocme in 'H'*140 + 'T'*110:\n", " suite.Update(outocme)\n", "myplot.Pmf(suite)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEACAYAAACtVTGuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOIwZgPKJcQAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the probability that the coin is fair? I.e., what is the prob. that x is 50%?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "suite.Prob(50)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 48, "text": [ "0.02097652612954468" ] } ], "prompt_number": 48 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Hypothesis testing" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load euro2.py" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "class Euro(thinkbayes.Suite):\n", "\n", " def Likelihood(self, hypo, data):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " hypo: integer value of x, the probability of heads (0-100)\n", " data: string 'H' or 'T'\n", " \"\"\"\n", " x = hypo / 100.0\n", " heads, tails = data\n", " like = x**heads * (1-x)**tails\n", " return like\n", "\n", "\n", "def AverageLikelihood(suite, data):\n", " \"\"\"Computes the average likelihood over all hypothesis in suite.\n", "\n", " Args:\n", " suite: Suite of hypotheses\n", " data: some representation of the observed data\n", "\n", " Returns:\n", " float\n", " \"\"\"\n", " total = 0\n", "\n", " for hypo, prob in suite.Items():\n", " like = suite.Likelihood(hypo, data)\n", " total += prob * like\n", "\n", " return total" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "fair = Euro()\n", "fair.Set(50, 1)\n", "\n", "bias = Euro()\n", "for x in range(0, 101):\n", " if x != 50:\n", " bias.Set(x, 1)\n", "bias.Normalize()\n", "\n", "data = 140, 110\n", "\n", "like_bias = AverageLikelihood(bias, data)\n", "print 'like_bias', like_bias\n", "\n", "like_fair = AverageLikelihood(fair, data)\n", "print 'like_fair', like_fair\n", "\n", "ratio = like_bias / like_fair\n", "print 'Bayes factor', ratio" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "like_bias 2.57964902292e-76\n", "like_fair 5.52714787526e-76\n", "Bayes factor 0.466723359161\n" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "bias = Euro()\n", "\n", "for x in range(40, 70):\n", " if x != 50:\n", " bias.Set(x, 1)\n", "bias.Normalize()\n", "\n", "like_bias = AverageLikelihood(bias, data)\n", "print 'like_bias', like_bias\n", "\n", "ratio = like_bias / like_fair\n", "print 'Bayes factor', ratio" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "like_bias 8.89531486664e-76\n", "Bayes factor 1.60938608255\n" ] } ], "prompt_number": 58 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "SAT scores" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import csv\n", "import math\n", "import numpy\n", "import sys\n", "\n", "import matplotlib\n", "\n", "import myplot\n", "\n", "\n", "def ReadScale(filename='sat_scale.csv', col=2):\n", " \"\"\"Reads a CSV file of SAT scales (maps from raw score to standard score).\n", "\n", " Args:\n", " filename: string filename\n", " col: which column to start with (0=Reading, 2=Math, 4=Writing)\n", "\n", " Returns: thinkbayes.Interpolator object\n", " \"\"\"\n", " def ParseRange(s):\n", " t = [int(x) for x in s.split('-')]\n", " return 1.0 * sum(t) / len(t)\n", "\n", " fp = open(filename)\n", " reader = csv.reader(fp)\n", " raws = []\n", " scores = []\n", "\n", " for t in reader:\n", " try:\n", " raw = int(t[col])\n", " raws.append(raw)\n", " score = ParseRange(t[col+1])\n", " scores.append(score)\n", " except:\n", " pass\n", "\n", " raws.sort()\n", " scores.sort()\n", " return thinkbayes.Interpolator(raws, scores)\n", "\n", "\n", "def ReadRanks(filename='sat_ranks.csv'):\n", " \"\"\"Reads a CSV file of SAT scores.\n", "\n", " Args:\n", " filename: string filename\n", "\n", " Returns:\n", " list of (score, freq) pairs\n", " \"\"\"\n", " fp = open(filename)\n", " reader = csv.reader(fp)\n", " res = []\n", "\n", " for t in reader:\n", " try:\n", " score = int(t[0])\n", " freq = int(t[1])\n", " res.append((score, freq))\n", " except ValueError:\n", " pass\n", "\n", " return res\n", "\n", "\n", "def DivideValues(pmf, denom):\n", " \"\"\"Divides the values in a Pmf by denom.\n", "\n", " Returns a new Pmf.\n", " \"\"\"\n", " new = thinkbayes.Pmf()\n", " denom = float(denom)\n", " for val, prob in pmf.Items():\n", " x = val / denom\n", " new.Set(x, prob)\n", " return new\n", "\n", "\n", "class Exam(object):\n", " \"\"\"Encapsulates information about an exam.\n", "\n", " Contains the distribution of scaled scores and an\n", " Interpolator that maps between scaled and raw scores.\n", " \"\"\"\n", " def __init__(self):\n", " self.scale = ReadScale()\n", "\n", " scores = ReadRanks()\n", " score_pmf = thinkbayes.MakePmfFromDict(dict(scores))\n", "\n", " self.raw = self.ReverseScale(score_pmf)\n", " self.max_score = max(self.raw.Values())\n", " self.prior = DivideValues(self.raw, denom=self.max_score)\n", "\n", " def Lookup(self, raw):\n", " \"\"\"Looks up a raw score and returns a scaled score.\"\"\"\n", " return self.scale.Lookup(raw)\n", " \n", " def Reverse(self, score):\n", " \"\"\"Looks up a scaled score and returns a raw score.\n", "\n", " Since we ignore the penalty, negative scores round up to zero.\n", " \"\"\"\n", " raw = self.scale.Reverse(score)\n", " return raw if raw > 0 else 0\n", " \n", " def ReverseScale(self, pmf):\n", " \"\"\"Applies the reverse scale to the values of a PMF.\n", "\n", " Args:\n", " pmf: Pmf object\n", " scale: Interpolator object\n", "\n", " Returns:\n", " new Pmf\n", " \"\"\"\n", " new = thinkbayes.Pmf()\n", " for val, prob in pmf.Items():\n", " raw = self.Reverse(val)\n", " new.Incr(raw, prob)\n", " return new\n", "\n", "\n", "class Sat(thinkbayes.Suite):\n", " \"\"\"Represents the distribution of efficacy for a test-taker.\"\"\"\n", "\n", " def __init__(self, exam):\n", " thinkbayes.Suite.__init__(self)\n", "\n", " self.exam = exam\n", "\n", " # start with the prior distribution\n", " for p, prob in exam.prior.Items():\n", " self.Set(p, prob)\n", "\n", " def Likelihood(self, hypo, data):\n", " \"\"\"Computes the likelihood of a test score, given efficacy.\"\"\"\n", " p = hypo\n", " score = data\n", " raw = self.exam.Reverse(score)\n", "\n", " yes, no = raw, self.exam.max_score - raw\n", " like = p**yes * (1-p)**no\n", " return like\n", "\n", "\n", "def MakeFigures(exam, alice, bob):\n", " formats=['png']\n", "\n", " myplot.Pmf(exam.prior, label='prior')\n", " myplot.Save(root='sat_prior',\n", " formats=formats,\n", " xlabel='p',\n", " ylabel='PMF')\n", "\n", "\n", " myplot.Clf()\n", " myplot.Pmfs([alice, bob])\n", " myplot.Save(root='sat_posterior',\n", " formats=formats,\n", " xlabel='p',\n", " ylabel='PMF')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "code", "collapsed": false, "input": [ "def PmfProbGreater(pmf1, pmf2):\n", " \"\"\"Probability that a value from pmf1 is more than a value from pmf2.\n", "\n", " Args:\n", " pmf1: Pmf object\n", " pmf2: Pmf object\n", "\n", " Returns:\n", " float probability\n", " \"\"\"\n", " total = 0.0\n", " for v1, p1 in pmf1.Items():\n", " for v2, p2 in pmf2.Items():\n", " if p1 > p2:\n", " total += p1*p2\n", " return total\n", "\n", "exam = Exam()\n", "\n", "alice = Sat(exam)\n", "alice.name = 'alice'\n", "alice.Update(780)\n", "\n", "bob = Sat(exam)\n", "bob.name = 'bob'\n", "bob.Update(760)\n", "\n", "print 'Prob Alice is \"smarter\":', PmfProbGreater(alice, bob)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Prob Alice is \"smarter\": 0.572030956616\n" ] } ], "prompt_number": 65 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }