From 98b9462b9ab02b435c225788652a282199b7dc13 Mon Sep 17 00:00:00 2001 From: Jeremy Teitelbaum Date: Sun, 15 Apr 2018 11:48:50 -0400 Subject: [PATCH 1/2] work on 3.10.11 --- BDA 3.10.11.ipynb | 86 ++++++++++++++++++++--------------------------- gelman.r | 4 ++- 2 files changed, 40 insertions(+), 50 deletions(-) diff --git a/BDA 3.10.11.ipynb b/BDA 3.10.11.ipynb index 4c37fc8..62dec8a 100644 --- a/BDA 3.10.11.ipynb +++ b/BDA 3.10.11.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -60,30 +60,30 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.599274021823669\n", - "[0.96947339 1.55234798 2.69693603]\n" + "2.6356753062363643\n", + "[0.95549603 1.54734748 2.68231072]\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -129,12 +129,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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PWYSIgl7Sbq7BPV8O67UW9COBwwkzbr7+euRipJ4p6CX91iftG1fGrKL3+gFjk/XctxKRCBT0km7tdN5V6oTuNkypsUm7bFnMKqTOKegl3V4D9hGGQY6IXMuhGJu0CnqJSEEv6bYhaWuxNw8KekmFkoLezKab2VozazGzq4q8/hdmttLMlpvZo2Y2sfylSl1an7RjI9bQF8cRTgndsAHeeCN2NVKnegx6M2sAbgQuBiYClxUJ8rnuPtndzwCuA/6x7JVKfcr16MfGLKIP+tH5bUQHZCWSUnr004AWd1/n7nuBu4BL8zdw9x15D48AdP806buOjs4efa0O3YCGbyS6UuajH0XneQ8ArcB7Cjcys78EriR8Uf1QWaqT+vbyy2Fe96HAsNjF9MHYpFWPXiIppUdvRZ47qMfu7je6+0nAt4HvFt2R2Swzazaz5ra2tt5VKvVn6dLQjotbRp+NTVr16CWSUoK+lQOnkhpNOOmtK3cBf1LsBXe/yd2b3L2psbGx9CqlPj31VGhPiltGn40GBg6EF16AHTt63Fyk3EoJ+qXAeDMbZ2YDgZnAvPwNzGx83sP/ArxYvhKlbmUl6PsDZ5wR1nPfUkSqqMegd/d2YDawAFgD3O3uq83sGjObkWw228xWm9lywjj95RWrWOrDrwyWLgnrJ8YtpSzekxzWevLJuHVIXSrp5uDuPh+YX/DcnLz1vypzXVLvWoG9wDHAuyLXUg7nnAM/+Qk88UTsSqQO6cpYSad1SVvrwzY5+T1619nHUl0Kekmnl5I2K0F/4okwYgRs3gzr18euRuqMgl7SqSVpsxL0Zhqnl2gU9JI+b70FG4EGavuK2ELnnBNajdNLlSnoJX2am8MleWOorXvE9kQ9eomkpLNuRKpibnIR9m+SxydHq6Qypk0LQzjLlsGePTBoUOyKpE6oRy/p80LSnhK1ivIbOhROPRX27oXly2NXI3VEQS/p0kFn0GepRz/XwpIbp3/88bj1SF1R0Eu6vAK8AzQCwyPXUgn9/zW0ixfHrUPqioJe0iWLvfl8pybtI4/owimpGgW9pEtWx+dzjgGOArZsgTVrYlcjdUJBL+nhwPPJelaD3oAJyfojj8SsROqIgl7SYwuwlXAzyuMi11JJ+cM3IlWgoJf0eC5pTyHbv5kap5cqy/LHSWrN6qQ9LWoVlTeKMMHZxo2wbl2Pm4v0lYJe0sG9s0ef9aA34Pzzw/rDD0ctReqDgl7SYe3aMD4/hHCP1ay74ILQLloUtQypDwp6SYdc4J1G6PFm3Uc+EtoHH4SOjri1SOaVFPRmNt3M1ppZi5ldVeT1K83sOTNbYWaLzCxLk8tKNeQHfT045RQYPRra2mDFitjVSMb1GPRm1gDcCFwMTAQuM7OJBZs9AzS5+xTgXuC6chcqGbZ/P/zhD2G9XoLerLNXv3Bh3Fok80rp0U8DWtx9nbvvBe4CLs3fwN0fcvddycMnqI9RVimX7/eHrVthBDAydjFVMtdg8C1h/cEH49YimVdK0I8CXs173Jo815UvAQ/0pSipM88k7ZSoVVTfJELPfvFi2L07djWSYaUEfbFDY0Wv8jCzzwNNwPVdvD7LzJrNrLmtra30KiXbnk3aqVGrqL53AVOnhpuQaDZLqaBSgr6VcFO3nNHAa4UbmdmFwNXADHffU2xH7n6Tuze5e1NjY+Oh1CtZs3kzrAMGEI4A1ZvcOP38+XHrkEwrJeiXAuPNbJyZDQRmAvPyNzCzqcDPCSG/ufxlSmY98ED4fjgBOCx2MRF8/OOhnTdP0yFIxfQY9O7eDswGFgBrgLvdfbWZXWNmM5LNrgeOBO4xs+VmNq+L3Ykc6P77Q3tG3DKiWXduuEjs5Zdh1arY1UhGlXRzcHefD8wveG5O3vqFZa5L6sG+fbBgQVivt/H5nH6E//aHCb36yZMjFyRZpCtjJZ7Fi2HHjjAlcb2cVlnMWUl7331Ry5DsUtBLPPfcE9qz45YR3SRgILB0Kbx20HkOIn2moJc49u+HuT8L69PilhLdIELYQxi+ESkzBb1U31yD/9kfdhDuoaqZkTq/1fz611HLkGxS0EscTybtNOpjtsqeNAGDBoX56TV8I2WmoJfq6yBcnQHwnpiFpMhg4JJLwrn0d98duxrJGAW9VN8aYDvQCIyNW0qqjP5NaO+8M24dkjkKeqm+3LQu56Fhm3xTCVcHP/WU7iUrZaWgl+p66y14Klk/P2ol6TOIznPq77gjZiWSMQp6qa577oE9wCmEM27kQOcl7U/m6BaDUjYKeqmuW28N7QeiVpFekwjHLtqA3/8+cjGSFQp6qZ6WljDtwSB0kVRX+tH5R/Dmm2NWIhmioJfq+Zd/Ce17gMOjVpJuHyAcpL7vvjBfv0gfKeilOnbuhF/+Mqx/JG4pqTeccAbOvn3wVR3IkL5T0Et1fOVdYabKk4FxsYupAbmJvxcCe/fGrEQyQEEvleceAgvgo1ErqR1TCDft3Irmv5E+U9BL5T3wQLjL8DDCnC7SMwMuTtZ/9CPdZlD6REEvleUO114b1qdT4j3NBIBzgaHAs8/CokWxq5EapqCXylq0CJ54ItxRWDec7J0BdB64nn0R3KH5IuTQlBT0ZjbdzNaaWYuZXVXk9feb2TIzazezT5W/TKlZud78JYR5XKR3PkL4I7kW0L3D5RD1GPRm1gDcSBgxnAhcZmYTCzZ7BfgiMLfcBUoNW7QIHnkEjjoKLopdTI0aDHwsWb8HjdXLISmlRz8NaHH3de6+F7gLuDR/A3df7+4rCDONi4RbBX7962H9m98MgSWH5iJgCPASutWgHJJSgn4U8Gre49bkuV4zs1lm1mxmzW1tbYeyC6kVv/pVOIg4Zgx87Wuxq6lth9HZtfrGN2DPnpjVSA0qJeiLHQE6pO+P7n6Tuze5e1NjY+Oh7EJqwc6dcPXVYf3734fDNd9Bn10IHEeYL+iGG2JXIzWmlKBvBcbkPR5NOCtapLg5c+D116GpCS67LHY12dAfuDxZ/9734J8t3GRdpASlBP1SYLyZjTOzgcBMQAOFUtxTT8GPfwz9+sHPfgZ3NSiQymUScDbw9ttwK4f4vVrqUY9B7+7twGxgAeFun3e7+2ozu8bMZgCY2dlm1gp8Gvi5ma2uZNGSUnv3whVXhBtmXNwBa3UZbNn9N8LMn08Dj0auRWpGSdcpuvt8YH7Bc3Py1pcShnSknn33u7ByJZx0EnzypdjVZNPRwBeAnwO/Aq5uhdH66En3dGWs9N1cg28ZXH89NDSEM24GxS4qw84HzgR2EY6B7NsXuSBJOwW99N0bwM+S9e99D973vpjVZJ8BVxAmiXv0Ufj2tyMXJGmnoJfem5t3xsfOnfAPwFvAZGD0d3TwtRqGAl8FGginW87VRenSNQW9HLr9+8PQwSvAsYRD9vqNqp6Tgc8n65d/Dq7WKZdSnD6Wcmg6gC99CX772zDp1jcIrVTXRYTpn9uBG4D1UauRlFLQS+91ALcAt90WDrpeCbw7bkl1y4DPEW64vhv4e2Dp0qglSfoo6KV39uyBnwIPEeZL/zpwStyS6l4/4MvAWcDbwIUXwpIlcWuSVFHQS3Fzi4z3btsG06fDY4SJtr4BnBahNjnYAMLB2fcQbsL+wfPgjjsO3Cb3/1Tj+HVHQS+lWbYMTh0Gf/gDHAX8D8Il+ZIe/QkHxD8E7AM+//lw6mV7e9y6JDoFvXSvowNuvBHe+17YBBwP/C0wNmpV0pV+wJ8RbgPU0ADXXQfnnw8v6Urleqagl669BnzwgzB7dpjH5sPA3wGaYTrdjHA2zqJFMGpUuGfv6afD74D9kWuTKEqa60bqzNat4aaQC4F9j8DIkaFXv/fTsSuT3vjAB8LcQ5cMhyfehtsJB9G/ELswqTb16KXT9u3wwx+GScnuJ4zzvh+4drNCvhbNNXhgeBi3vxIYSbi7xN8TzsxZvDhqeVI96tELvPwy/NM/wS9+EaY0gOQ28MCJMQuTsjDCqZeTCX/A7ycM6yxaFE6NvRD40TswSDPRZZWCvl5t3w733gu33w4PP9z5/ETg44RQ0Fl42TIQ+ATwEcJ4/e+Atcly9xj4whfgM5+Bs88G0//8LDH3OLepaWpq8ubm5ijvXbfWrYP58+H+++GhhzpvMn3YYfDpT4ebeK85M26NUj27gSXAIsJ8RTknnACf+hR89KNw3nm652/KmNnT7t6ru/oo6LOqowOefx4eeyxcJblkCbz44oHbTADOA6YBVyS/B7qYpv440AI8Dqw8Dl7LuyX0oEFw7rlwwQUwbVro7Q8fHqdOART09ckdNm2CF14IZ1jklmceCz22fEOGhF7a8HvgdMJUtyL5Zu6Hxx+HefPgnuvCJGmFEXHSSXDmmTBhAkycGNqTTw7fDKXiDiXoSxqjN7PpwI8Js1//wt1/UPD6IMKNzc4i3IbiT919fW8KkSI6OuDNN0OQ55bXXw8HT/OX3YWJnhhGmMo2t3x7CwwYoF67dK1fv9CDP/dcOP26cJ+B54AXgR3nwtNPh4uvCi/AMmA4cOq5Yejn+OM725Ejw9LYqGGgSHrs0ZtZA/AC4RKMVmApcJm7P5e3zX8Hprj7X5jZTOAT7v6n3e03sz1693Bx0e7dYdm1q3M9t+zYEQ6GFi7btnW2mzZBW1tpl68PGwbDtsIY4NJ/hMmT4cWL1GOX8msHpjwDK1bAmjWw8AfhwrpNHNzzL+bIIztD/+ijYejQsAwZcnA7ZAgMHhy+KRx++MFLv/o8O7xSPfppQIu7r0ve5C7gUsLf+ZxLCRfGA9wL/LOZmXf3V2TbtnDWR0dHWNw71wsfV+q1/fvD/Tbb20Nb6nrh4z17Dgzycg6HDSYE9lBgymfgmGPgzZ+Eq1Mbgb/cFj4Yf+ylXwmbUchLZfQHnpsa2snJAuEPwBvAlmR5I699C9iRLDt3hmXdur7XMnBgCPzcH4IBA7pf+vfv/rV+/Q5eGhpKe66nbc0OXODg50pdDkEpQT8KeDXvcSthjryi27h7u5ltJ9yvfkuXe33ppXCmRxY1EE5lyy0DCh4fTgjwXJu/HA4cAQxJlgH5O7774Pe6/6iK/CeI9Ep/4Jhk6YoTbmj+FrAd2Ek4jrQ7eX53kcd7k2VfsfW9Ydm+vQL/QdlSStAX+xNS2GUtZRvMbBYwK3m4x2BVCe9fTSPo7o9TqfbT+Qvbd+WpqfzSWJdqKo1qKl0a6+r1HSBKCfpWwuhvzmjCqFyxbVrNrD9h4ODNwh25+03ATQBm1tzbcaZKU02lS2Ndqqk0qql0aazLzHp9cLOUoxlLgfFmNs7MBgIzgXkF28wDLk/WPwX8R7fj8yIiUjU99uiTMffZwALC6PMt7r7azK4Bmt19HvBL4HYzayH05GdWsmgRESldSefRu/t8YH7Bc3Py1t8Bentk9aZebl8Nqql0aaxLNZVGNZUujXX1uqZoV8aKiEh11OcVByIidSRK0JvZUWZ2r5k9b2ZrzOy9MerIq+cUM1uet+wws7+OWVNS19fMbLWZrTKzO80s+mQiZvZXST2rY/4bmdktZrbZzFblPTfczB40sxeTdlgKavp08m/VYWZVP3uji5quTz57K8zsN2ZW1Ysxuqjp2qSe5Wa20MyOi11T3mvfMDM3sxHVrKmruszsb81sY15eXdLTfmL16H8M/M7dTyVMr7UmUh0AuPtadz/D3c8gzNezC/hNzJrMbBTwVaDJ3ScRDoRHPchtZpOAPydcLX068DEzGx+pnFuB6QXPXQUscvfxhMl3r0pBTauA/wo8UuVacm7l4JoeBCa5+xTC9CbfSUFN17v7lOQz+FtgzkE/Vf2aMLMxhOlfXil8rUpupUhdwA25zEqOoXar6kFvZkMIN6j7JYC773X3bdWuoxsfBl5y9w2xCyEcLD88uTZhMAdfv1BtE4An3H2Xu7cDDxNuZVF17v4IB1+rcSlwW7J+G/AnsWty9zXuvraadRS8f7GaFib//wCeIFwbE7umHXkPj6C0mXMqWlPiBuBb1a4np5u6eiVGj/5EoA34VzN7xsx+YWZHRKijKzOBO2MX4e4bgX8g9CReB7a7+8K4VbEKeL+ZHW1mg4FLOPBiutiOcffXAZJ2ZOR6asGfAQ/ELgLAzP6Xmb0KfI4X06A/AAACGElEQVTq9+iL1TMD2Ojuz8aupYjZyVDXLaUMUcYI+v7AmcBP3X0q8DbV/4pdVHJB2AzgnhTUMozQQx0HHAccYWafj1mTu68Bfkj46v874FnCdFZSg8zsasL/vzti1wLg7le7+xhCPbNj1pJ0ZK4mBX9wivgpcBJwBqET+KOefiBG0LcCre7+ZPL4XkLwp8HFwDJ33xS7EMItm1929zZ33wf8P+B9kWvC3X/p7me6+/sJXylf7OlnqmiTmR0LkLSbI9eTWmZ2OfAx4HMpvIp9LvDJyDWcROhkPWtm6wnDW8vM7N1RqwLcfZO773f3DuBmwjGzblU96N39P4FXzSw3Mc+HOXDK45guIwXDNolXgHPMbLCZGeHfKepBawAzG5m0xxMOMqbl3wsOnIrjcuC+iLWkVnIjoW8DM9x9V+x6AAoO6s8Ano9VC4C7r3T3ke4+1t3HEjqoZyb5FVWuM5P4BKVMDunuVV8IXzmagRXAvwHDYtRRUNNgwuzZQ2PXklfT3xF+4VcBtwODUlDTYsIf5meBD0es407C19Z9hA/hlwhTYy8ifMtYBAxPQU2fSNb3EG7PsSAFNbUQphVfniw/S0FN/zf5PV8B/DswKnZNBa+vB0ZUs6Zu/q1uB1Ym/1bzgGN72o+ujBURyThdGSsiknEKehGRjFPQi4hknIJeRCTjFPQiIhmnoBcRyTgFvYhIxinoRUQy7v8DNjuH3fe8s6YAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -205,14 +205,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/jteitelbaum/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log\n", + "/home/jet08013/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log\n", " \n" ] }, @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -249,7 +249,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jteitelbaum/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n", + "/home/jet08013/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n", " from ipykernel import kernelapp as app\n" ] }, @@ -282,64 +282,52 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-2. , -2. , -2. , ..., -2. ,\n", - " -2. , -2. ],\n", - " [-1.9798995 , -1.9798995 , -1.9798995 , ..., -1.9798995 ,\n", - " -1.9798995 , -1.9798995 ],\n", - " [-1.95979899, -1.95979899, -1.95979899, ..., -1.95979899,\n", - " -1.95979899, -1.95979899],\n", - " ...,\n", - " [ 1.95979899, 1.95979899, 1.95979899, ..., 1.95979899,\n", - " 1.95979899, 1.95979899],\n", - " [ 1.9798995 , 1.9798995 , 1.9798995 , ..., 1.9798995 ,\n", - " 1.9798995 , 1.9798995 ],\n", - " [ 2. , 2. , 2. , ..., 2. ,\n", - " 2. , 2. ]])" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "Y\n", + "u=list(map(sum,Z2))\n", "\n" ] }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.82616995 1.33837647 1.80934486 2.49570902 5.68990452]\n" + "1.437043700373185 [0.68945124 0.99000008 1.26005551 1.65286899 3.30679651]\n" ] } ], "source": [ "Psigma=list(map(sum,np.exp(Z2-np.max(Z2))))\n", "p=Psigma/sum(Psigma)\n", - "s=np.random.choice(np.exp(np.linspace(-2,2,200)),p=p,size=1000)\n", - "print(np.percentile(s,q=[2.5,25,50,75,97.5]))" + "s=np.random.choice(np.exp(np.linspace(-2,4,200)),p=p,size=10000)\n", + "print(np.mean(s),np.percentile(s,q=[2.5,25,50,75,97.5]))" ] }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.371512604377929 [0.61111977 0.9320659 1.18631786 1.60377754 3.20858216]\n" + ] + } + ], "source": [ - "p=[1/3,1/3].append([1/200.0]*198)" + "Psigma=list(map(sum,np.exp(Z1-np.max(Z1))))\n", + "p=Psigma/sum(Psigma)\n", + "s=np.random.choice(np.exp(np.linspace(-2,4,200)),p=p,size=10000)\n", + "print(np.mean(s),np.percentile(s,q=[2.5,25,50,75,97.5]))" ] }, { @@ -595,7 +583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/gelman.r b/gelman.r index f39d03f..dcfcd05 100644 --- a/gelman.r +++ b/gelman.r @@ -33,4 +33,6 @@ ldens} mu <- mugrid[muindex] sd <- rep (NA,nsim) for (i in (1:nsim)) sd[i] <- exp (sample(logsdgrid, 1, prob=dens[muindex[i],])) - kprint (rbind (summ(mu),summ(sd))) \ No newline at end of file + print (rbind (summ(mu),summ(sd))) + + From c2c045c06b27116c398b0150863fb9f47ee3e76e Mon Sep 17 00:00:00 2001 From: Jeremy Teitelbaum Date: Sun, 15 Apr 2018 14:53:39 -0400 Subject: [PATCH 2/2] Added solution to 3.10.8 using Stan --- BDA 3.10.8 - Stan.ipynb | 299 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 299 insertions(+) create mode 100644 BDA 3.10.8 - Stan.ipynb diff --git a/BDA 3.10.8 - Stan.ipynb b/BDA 3.10.8 - Stan.ipynb new file mode 100644 index 0000000..7f4afdc --- /dev/null +++ b/BDA 3.10.8 - Stan.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_b2ef2a6e4a8e372508b34c24c518abeb NOW.\n" + ] + } + ], + "source": [ + "\n", + "# coding: utf-8\n", + "\n", + "# #### Problem 3.10.8\n", + "# \n", + "# Analysis of proportions: a survey was done of bicycle and \n", + "# other vehicular traffic in the neighborhood of the campus of the \n", + "# University of California, Berkeley, in the spring of 1993. \n", + "# Sixty city blocks were selected at random; each block was observed \n", + "# for one hour, and the numbers of bicycles and other vehicles traveling \n", + "# along that block were recorded. The sampling was stratified into six \n", + "# types of city blocks: busy, fairly busy, and residential streets, with \n", + "# and without bike routes, with ten blocks measured in each stratum. \n", + "# Table 3.3 displays the number of bicycles and other vehicles \n", + "# recorded in the study. For this problem, restrict your attention \n", + "# to the first four rows of the table: the data on residential streets. \n", + "# \n", + "# (a) Let $y_1$ , . . . , $y_{10}$ and $z_1$ , . . . , $z_8$ be the \n", + "# observed proportion of traffic that was on bicycles in the residential \n", + "# streets with bike lanes and with no bike lanes, respectively \n", + "# (so $y_1 = 16/(16 + 58)$ and $z_1 = 12/(12 + 113)$, for example). \n", + "# Set up a model so that the $y_i$ ’s are independent and identically \n", + "# distributed given parameters $\\theta_y$ and the $z_i$ ’s are \n", + "# independent and identically distributed given parameters $\\theta_z$ . \n", + "# \n", + "# (b) Set up a prior distribution that is independent in \n", + "# $\\theta_y$ and $\\theta_z$ . \n", + "# \n", + "# (c) Determine the posterior distribution for the parameters \n", + "# in your model and draw 1000 simulations from the posterior distribution. \n", + "# (Hint: $\\theta_y$ and $\\theta_z$ are independent in the posterior \n", + "# distribution, so they can be simulated independently.) \n", + "# \n", + "# (d) Let $\\mu_y = E(y_i |\\theta_y )$ be the mean of the distribution \n", + "# of the $y_i$ ’s; $\\mu_y$ will be a function of $\\theta_y$. \n", + "# Similarly, define $\\mu_z$ . Using your posterior simulations from (c), \n", + "# plot a histogram of the posterior simulations of $\\mu_y-\\mu_z$, the \n", + "# expected difference in proportions in bicycle traffic on residential \n", + "# streets with and without bike lanes. We return to this example in \n", + "# Exercise 5.13.\n", + "# \n", + "# Gelman, Andrew; Carlin, John B.; Stern, Hal S.; Dunson, David B.; \n", + "# Vehtari, Aki; Rubin, Donald B.. Bayesian Data Analysis, \n", + "# Third Edition (Chapman & Hall/CRC Texts in Statistical Science) (Page 81). \n", + "# CRC Press. Kindle Edition. \n", + "# \n", + "# #### Data\n", + "# |Type |Bike lane? |Counts of Bikes/others|\n", + "# |--- |----------|----|\n", + "# |Residential |yes |16/58, 9/90, 10/48, 13/57, 19/103, 20/57, 18/86, 17/112, 35/273, 55/64 |\n", + "# |Residential |no |12/113, 1/18, 2/14, 4/44, 9/208, 7/67, 9/29, 8/154|\n", + "# \n", + "# Gelman, Andrew; Carlin, John B.; Stern, Hal S.; \n", + "# Dunson, David B.; Vehtari, Aki; Rubin, Donald B.. \n", + "# Bayesian Data Analysis, Third Edition (\n", + "# Chapman & Hall/CRC Texts in Statistical Science) \n", + "# (Page 81). CRC Press. Kindle Edition. \n", + "\n", + "# #### Probably best to first do 3.10.6\n", + "# For that problem see the reference \n", + "# [Raftery, 1988](https://www.stat.washington.edu/raftery/Research/PDF/bka1988.pdf)\n", + "import pystan\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import beta\n", + "\n", + "stan_code=\"\"\"\n", + "data {\n", + " int N;\n", + "\n", + " int bikes[N];\n", + " int others[N];\n", + "}\n", + "\n", + "parameters {\n", + " real theta_b;\n", + " real theta_v;\n", + "\n", + "}\n", + "\n", + "model {\n", + " //theta_b~uniform(0,100);\n", + " //theta_v~uniform(50,300);\n", + " \n", + " bikes~poisson(theta_b);\n", + " others~poisson(theta_v);\n", + "}\n", + "\n", + "generated quantities {\n", + " real b_ppc;\n", + " real o_ppc;\n", + " real p ; \n", + "\n", + " o_ppc=poisson_rng(theta_v);\n", + " b_ppc=poisson_rng(theta_b);\n", + "\n", + " p=o_ppc/(o_ppc+b_ppc);\n", + "}\n", + "\"\"\"\n", + "sm=pystan.StanModel(model_code=stan_code)\n", + "fit=sm.sampling(data=dict({'N':10,'bikes':[16,9,10,13,19,20,18,17,35,55],'others':[58, 90, 48, 57, 103, 57, 86, 112, 273, 64] }))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "fit=sm.sampling(data=dict({'N':10,'bikes':[16,9,10,13,19,20,18,17,35,55],'others':[58, 90, 48, 57, 103, 57, 86, 112, 273, 64] }))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "b_ppc=fit.extract()['b_ppc']\n", + "o_ppc=fit.extract()['o_ppc']\n", + "theta_b=fit.extract()['theta_b']\n", + "theta_v=fit.extract()['theta_v']\n", + "p_lane=fit.extract()['p']\n", + "%matplotlib inline\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax=plt.subplots(1,2)\n", + "ax[0].set_xlim(15,30)\n", + "ax[1].set_xlim(80,115)\n", + "j=ax[0].hist(theta_b,density=True,bins=40)\n", + "j=ax[1].hist(theta_v,density=True,bins=40)\n", + "ax[1].set_xlabel('theta_v has mean '+str(np.round(np.mean(theta_v),2)))\n", + "ax[0].set_xlabel('theta_b has mean '+str(np.round(np.mean(theta_b),2)))\n", + "plt.show()\n", + "fig,ax=plt.subplots(1)\n", + "s=ax.hist(1-p_lane,bins=50,density=True)\n", + "ax.plot(np.linspace(0,1,1000),beta.pdf(np.linspace(0,1,1000),b=94.91,a=21.3))\n", + "ax.set_title('Proportion of bicycles with no bike lane')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "bikes=[12,1,2,4,9,7,9,8]\n", + "other=[113,18,14,44,208,67,29,154]\n", + "fit=sm.sampling(data=dict({'N':8,'bikes':bikes,'others':other }))\n", + "b_ppc=fit.extract()['b_ppc']\n", + "o_ppc=fit.extract()['o_ppc']\n", + "theta_b=fit.extract()['theta_b']\n", + "theta_v=fit.extract()['theta_v']\n", + "p_nolane=fit.extract()['p']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax=plt.subplots(1,2)\n", + "#ax[0].set_xlim(15,30)\n", + "#ax[1].set_xlim(80,115)\n", + "j=ax[0].hist(theta_b,density=True,bins=40)\n", + "j=ax[1].hist(theta_v,density=True,bins=40)\n", + "ax[1].set_xlabel('theta_v has mean '+str(np.round(np.mean(theta_v),2)))\n", + "ax[0].set_xlabel('theta_b has mean '+str(np.round(np.mean(theta_b),2)))\n", + "plt.show()\n", + "fig,ax=plt.subplots(1)\n", + "s=ax.hist(1-p_nolane,bins=50,density=True)\n", + "ax.plot(np.linspace(0,1,1000),beta.pdf(np.linspace(0,1,1000),b=81,a=6.6))\n", + "ax.set_title('Proportion of bicycles without a bike lane')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax=plt.subplots(1)\n", + "j=ax.hist(p_nolane-p_lane,bins=60,density=True)\n", + "ax.set_title('Difference in proportions with a lane vs without')\n", + "x=np.linspace(0,1,1000)\n", + "ax.set_xlabel('Difference has mean '+str(np.round(np.mean(p_nolane-p_lane),2))+' and sd '+str(np.round(np.sqrt(np.var(p_nolane-p_lane)),5)))\n", + "\n", + "plt.show()" + ] + }, + { + "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 +}