Created
March 14, 2017 05:27
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Test Notebook to demo plot behavior
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"import edward as ed\n", | |
"import numpy as np\n", | |
"import tensorflow as tf\n", | |
"import seaborn as sns\n", | |
"\n", | |
"from edward.models import Bernoulli, Beta\n", | |
"from edward.criticisms import ppc_stat_hist_plot\n", | |
"\n", | |
"%matplotlib inline" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x115a6d160>" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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hed/RweSjjxLauxe1VFoztFWnnM3i7+mh59Ah1HKZuV/9yng9lyMfjdJ96BDp\nubkN117N5bB7vQT6+ijEYmv0fWRFQVdVdtX7+R98sOm4IxBoUhR1t7c3OXXJNhsWq7WpCCwQNCIC\nv+B1g65pxk59g5x9I7lIZOPdfu1YvVe+3tseGxsj0N9PYOdOU9+mHqBbBgYYe/BB1FKJvR/4AI5A\ngPzKCvaaUFodrVo1C8IWq5WeQ4eQbTZmnniC5PQ0gZ07UdxuZEVZV0ZBq1ZRq1WsTieSxUJg505S\nZ86sycnbfT5aBgdxtLQw+fDDTU8VVrvd1AkCoz6xOtCLXb/gbIjAL3jdkItEcIZCG3rX1tF13Uzd\nrDmmaRQTCZwNhdtSOo0kSZTSafw7dmCr1QWiIyNoqoq/txeAl37wAwD63/lOfN3dxlPCquJyY/9+\nna6DB8mtrJBdWjKnfz2r0i/md1xZwdfdTbUWpK12O+7WVqNu0IAjEKCcybDz8GGyi4umCXud1T39\nno6Ops+z+3yUMxlR5BWsiwj8gtcFmqpSiMU23MU3UkqlsPt8TVO6dfLRKM5QqOlYIZEgH43i6+kx\nU0gWm82c9gXj5jBe6+bx9/ZSKRQIDg2ZPfLmZ2cy5ixAnezSEt0HD2Jzu1l+/nl0XV9jpl7/jsVk\nEm9Pz5rdebVYbHLUsrlcVAsF+g8fBl62f6yzurvH09FBtiHwS5K0JiUkENQRgV/wuiC3vIy7re2c\nzM/zsVjTjr5OpVAgOjJCOZcjPjFBYnKSxNQUiclJ8rGYubMv53KUs1nCe/eaRdKRH/8YtVRi93/4\nDzhbWiimUgSHhiil000TuZVcrqlwW8pkKKXT+Hp76b76aoqpFInJSeDlXb+maRSTSZaOHaOYShEb\nGSE5PU1yZsbM7wd27CA1O9uU0lE8Hnrf8hYAZn7xi6bBLNsqK0dnMEgpnW7a4TtDoQ2lJATbm03N\n1gWCC019JxweHt783GoVrVJp6o8vJpNkl5ZQKxVsbjee9nZkmw1d18lFIqb+fnJmBkmSyEWjdOzf\nj9XhID4+jrOl5eU0zw03YPf50MplFLcbb1eXMUjW32/q89RvTmq5THp2lmBNjE2yWuk+eJAzTz1l\nrqNaKjH/m9+YbaVdV1+NbLMZcgt2O7mVFdLlMoEdO3AGg001Drvfb+gB9fQw//TT5FZW8NYmlCVJ\nMkXbbC4Xss1myjfUC9JWu934fVWrm85DCLYXYscvuOjkVlZwNRRjz0YhkTBz+/Xp2EIiQcvgIHaf\nj9CuXWbRmLfWAAAgAElEQVRx1Wq3U0qlsHk8dB86ROvwMJIs425tJbu0RGJigko+T/T0acYffJDW\nSy4xdvmZDPaakJrD70ctl6kUCk3TurqmmeJxjaqeNpeLzquuYvHoURaPHsXh92P3+QyBtp4e81zF\n48Fis5kGLsnpaZAkComE2aNv93opZ7MMvOtdFBMJ5n75y6YngtXpHldr6xqtHtHTL1gPEfgFFxVN\nVSkmErjC4XM6vxCP4wwGUctlYmNjOEMhWnbuxCLLVGptknUqhQKLR49SzmZJz84aZulTU8g2G572\ndloGB/F2d/PcV7+KWi6z44YbsNhsZJeWTFNzwNz1l7NZ8/rJ6WlcodC64nGSJCHbbKZRus3tNs5v\nuGajB6/N6SS0e7eRupEks+NIsliQbTb63v52ABaOHaPc4O27usDrCoeb2jrBSAEVReAXrEIEfsFF\nJR+J4AqHz2m3r5bLSBYLuqYRn5jA39dn+tI2Pgnouk5mcZHF555D13X63vY2Qnv2YPf52HnkiKl7\nHx8fx+Z0svjsswAMvOtdSJLE8okTFOJxktPTZgqpnM2SnpvDoigkz5zBYrPhDAbRNQ1NVY2fatWQ\neIhG6X/nO3GGQqy8+CKyzUa1VGrqVlptvi5ZLAT6+7Ha7WQWFsxjdr+frquuAmDh6aebVETr6Zt6\nu6rd60XXtKa5gPoTxnrKoYLti0j8CS4adR378N6953R+IZHA7vOtET8DDJ39/n7USoXExIShVa9p\n+Pv6cLe1ka5r/8gyFpcLf18faqXC0rFjzDzxBOFLLqHjwAG8XV14u7roPHAAXdOoFouopRKyorB4\n7BhgFHi9PT3ExsYAzJtWZmEBJAl3WxvxsTFkq5WlF17AIsu07NxJIZHAWbs5WWQZfZWKpiRJBPr7\nKedyRE6douuqq3DUpnTDw8PM/vKXlNNpNFU1byL1Xb8zGMTmdGKxWimlUk0F6Hq6ZzOVU8H2QQR+\nwUWjEI/jaGk5p04ewFSh9HR0NAV9U7dGkoiPj+Pv7UWyWskuLNB73XVUSyWqNa2eRmSbjflnngFd\np/OKK6A2HyDb7aYnb30y2Op04gqHsTqddB861PSEous6yelpQrt34+3qAoxduK5pSHY7s08+ic3t\nZuG55wj09eEKh3G0tCDb7VRLpaYpZUmSaB0eZvKRR8jH47iCQdRKhf7Dh3n2H/6B5JkzeLq6zLZX\nh99PdnkZZzCIZLGgeL0UEglzHWDMBCSnpkTgF5iIVI/gonG26dvVqJUK+WgUu8dj7prrFJNJFI+H\nxMQEvp4e48/j4yherzEcNTtrat6vpm6ovu/WW4mcPk1qbq6pTlAnPjZmdAvVzN2bjo2PG08GlYrp\no5uYmiI1O0t2fh5vZyeRkydRy2WqpRLZpSVWXnzR+Ps6sgoWWabzqqtYeOYZdF3H7vXSU2vrXHj2\n2aYWzXq/f73oa/f5UMvlptSOme5pEHYTbG82DfyapnH33Xdz66238nu/93vMzMw0Hf/ud7/Lhz/8\nYW655Rb+rcEjVCA4G8VkEsXtPuc2w3q3irfWFtlIIR6nEI/j7eoyg3ZsdBR/fz+VXA6rw7Gupk9i\naorZp56i7/rr6bziClp27iT60kvQENg1VSU+MUE5n6fzyiuxe71m4C1lMsw88QTZxUVT5rnupRva\ntQtvZyeB/n6G3v1u/P39uGsy03a/H13TSE5Ps3j06Lp2ie5wGEcgQGJyErvfT8fll4MkMfOLXxia\nQw3OXI31gnq3UGO3D6wVdhNsbzYN/I888gjlcpn777+fu+66iy9+8YvmsXg8zve+9z2+//3v881v\nfpO/+qu/amo3Ewg2IreygvsVpB6ip07Reskla3bbaqVCPhLBGQyaMsh12WRvZyfZpaWmtEcjz3/n\nOwDG0FYwaFg8+nyUUimKqRSFRILYyAiOQABPWxsWWcbT0UFicpLIqVOsnDyJp72dHW9/O97OThSP\np2l92ZpCqEWWadu3j3IiQSmTwdPeTsfll9Nz6BDlXI6Jhx9eV6Wz7bLLiI2OIisKFquVziuvZO5X\nv0K225taNBuDuuJ2Y7FY1kzsiileQSObBv7nnnuO66+/HoADBw5w8uRJ81gwGOSBBx7AZrMRjUax\n2+3n1J0h2N6UczksVuumCpx1cisrWGR5TYoHDMMWajr4daKnT+Npa0OrVnG0tKz7VKHrOs9/61vI\nDgc7jxzB5nKhVavIsoy9pYXZp54iMz9PaPduZJsNxeMxunbOnEFTVUqZDN6uLkK7d6+75nq6pd7u\naff58Pf3k49GTU0dZzBIcHCQ3re9jYVnn2X55Mmm3b/icuHt7CQ+Po6sKOx4+9tRy2VWXnyxaffe\n2NZZ9ybQa51GdcxhLmHLKOAcAn82m8XT0KssyzLVhn+cVquV73znO9x6663cfPPNm37gfffdx549\ne5p+jhw58iqXL3gjkltZwd3Wdk7n6rpOfHyc4DoBVtc0IqdO0X7ZZeaGo5hOU0wkcHd0kBgfR61U\nSM7MkJiaMmUS0nNzjP30p8THxxk4fBjZZiM5M8PyiRNUikXK6TT973wnkixTLZUoZTJoqkpsdBRn\nKESgv59SMrmhzy+8vNtvJNDfj8PvJ3LypPlkbLFaUdxuBm+8kUoux8Jzz1Fq6NVvGRw0UkuSRPeh\nQwDM/PznWGw2M91jkWWQJPOmYXU41lUHFekeQZ1NA7/H4yHX0BesaRrWVTuoj3/84zzxxBM888wz\n/PrXvz7r9e68805GRkaafh5dZTQhePOyeie8GflIBMlqxbWOG1d6ft6YjK3l9XVdZ+HppynlcqTm\n5lA8Hhx+v2me4m5vxxUOY/f7OfWjHwGw88Yb8XZ3o3g8hPbsoXV42Oi4sdkIDg6Smpkh8uKLVAsF\nQrt3o6sqpXSa7kOHyGygua9Vq8aw1ypJ53rKp5TLkarVyqxOJ5VCAYvVStfVV2NzuUhMTpKenzcL\nu85amiY8PIzFZmPqscdMs5Y6jbt+m9tt5PnXS/eIwC/gHAL/lVdeyeOPPw7A8ePH2d2w85qcnOSO\nO+5A13VsNhuKomA5x9Y8wfbklXTy6JpGLhJBcbnWFGfVSoX03ByBnTsBqBaLLL/wAompKTr278fT\n2krbpZfi8PsNM3aHA5vTacg52Gyc/tGPcLe3s+s978EVCqFVqzhbWtB1HXdrK/loFIvVagyDLS3R\nMjhIOZcjF4kQHBw0B8fW09xf7QfQiCMQILRnD/PPPouuaU2FWYssE96zB9lmM317dU3D29ODpqpU\ncjm6Dx1i8bnnAJoCe6N8g+LxGDMIDd0+YEwIr35NsD3ZNErfeOONKIrCbbfdxhe+8AX+/M//nG98\n4xs8+uijDAwMsHfvXm699VZuu+029u/fzzXXXPNarFvwBkTXtA119NcjF4kYwXqd9srM/Dw2lwtH\nIEAhHicxNUUlnye0Zw9WRTGmgTfYhJx+4AGKiQR7P/ABUxCtlE4bKRKbDVcoRC4SIToyguJ2033N\nNSyfOEF6bo7g0JB5XV9Pj7Ezb1DENP0AzuIX3LpnDwDR0VEzGNeRFcUYRCuVcPj9xEZHsdhsaJUK\nmYUFOq66Cl3TmHn8cax2uynvXG/rrP+5ks+jeDxNUs9g3BQqqxy/BNuPTXvpLBYL99xzT9Nrg4OD\n5p/vuOMO7rjjjq1fmeBNR11n51waAHRdpxCLoXi9a/rqy7kc1XIZ2Wajks9TiMcNobPHH8fT0YGu\naWfV/jn61a8CsOfmmw0lTlVFV1V0TcPqcKDruqGb39Vl9O3LMqVUCsliabqZyIqCu62N1JkzBPr7\ngZpkdG2YqrGQKjWoesqKQteVV7Lw7LP4VmnzgxG4naEQqdlZAM488QSuUAjZbidYe8J58Z//mdZ9\n+ygkEuYwm83lolyTja4PcxWTyabfn732ZHCuqTbBmxMxuSt4zchFIoR27TqncwvxuJmT9q3q3c8s\nLODw+8mtrFCIxQgODbHy0ks4AgHTxHyj3X58YoKpxx6j561vpePAAcOZq2auUi0Wke12EhMThPfu\nJR+NGoHV4aDrqqtM/Z6WWvAFcIVClNJpsktLSLLM4rFj+Lq7KSQSxo2idpPTdd2Y5JUkZLsdm8eD\nLknERkawuVzGsdqa68Yx1VKJlv5+wnv2EJ+YMG5q1So2t5vFo0eR7XaWjx/HFQxidTiM4m06jeJ2\nm8G/UdQNDD2fzMICrDMPIdg+iMAveE0oZTLYXK5zHtjKRyL4ensp53JIFgtqpUIhHicfiZBeWECr\nVKjk84SHh1l6/nmyS0t42tvRVbVJBXM1x77+dQD2fvCD5lNBMZUy0jvRKOV0Gl9PD85gkMzCAsnp\naYY/8AEsVivutjbUuTnS8/P4urvRqlXyNbP02MgInq4uggMDtAwObvhUo+u64baVyeBtb2f2178m\nODCAt7MTq9NJ6swZrHa7cYOs3RicoRDBoSHOPPkkOtDzlrcw9eijWGQZX28v0ZERgkND2L1eU7O/\nntJp1OyHlxU/V0tFCLYXohIreE3Iraycc1G3lMlgdTioFgpY7XbDRWtiwmxb7H3rW9E1jd3vex+h\nXbuM/ntFITYyYsggNEy1NqJWKhz/xjdQfD6G3vMes+e9ksu9LJ0cDuMMBskuLVEpFHAGg003K19P\nD+VMhoVnnyU2NoZksdA6PEz/DTeQmpnB3dFx1lSWJEnYnE7cbW30XX89LTt3Ukqnmfr5z5n+xS9w\nBAL4+/qwWK1YZBlvdzfp2VlsTicdBw5QzmQI1Qxrph59FE9HB65QiNSZM+b31mpPBeVcbk33D4i2\nToEI/ILXgGqphK6q68omrEd9qjc+OUkhHscVDhPeu9fozrHbTe0d2WYjOTWFOxzG09GBt7ub8PAw\n2aUl4uPja3Lnpx94gOziIns/+EFTsK2Sz2N1OslHo6jlMp6ODpLT06iVCv7eXpwtLWbXjVatkp6b\nQ9d1JFnG096Ou7XVlFAI791LenaWaql0Tt9TkiRa+vuNPn1dp+/aaymlUsQnJsy1O2ouXMVkEndr\nK8HBQfOJZurRR3EEAlTyeYJDQ8bTgtNJKZ1GttnQqlUUr3dN55GjlhISbF9E4BdccPKRyFnTL43U\nhcxSs7NUcjnaL7/cLE5ml5aQ7XbKuRy+3l4j6Le1US0WqdSkEJwtLQQHB/F0dpKenSU1O2sWWZ++\n7z4A9n30o2Y7ZjGVwiLLxsBVW5vRyeP14u/tpZzN0jIwQHZpiUI8Tmx0FKvTSevwMO2XX04uEjFb\nKLNLSwR27CCwY4fh8RuNnvV7VkslElNTIEn4+/qQJAm1XCY4NISno4PUmTNmx5C/t5fMwgK6phHa\nvZv2fftQ/H4mHn7YbDmVatcpxGLmmqwOB2q5jKwoTU9BsqKgVipN3UiC7YUI/IILiq5pTT6wm5Fd\nXqacSmFzuWgZGDALntVikWq5TDmTwep0kltZMS0NwZjY9TXILituN6Hdu7G5XMRGRph58knOPPEE\nO97xDroPHjTPK8Tj5GMx9EqFUjpNy8AArlDICIq6juJ2k5icNArTe/bgqpm8W2SZ4OAg2cVFUnNz\nWJ1OZEXB5nIR3rPHNH7PrawYyp01+8Z8NEpsbIzUzAyucJjW4WHCe/dSKRRIz82hqaq5dtlmIzoy\nQqVQwBUOm/LLdp+PziuuILu4SOTUKbOHX/F48HV3m2bv9XZORyCwNt1Ts3UUbE9E4BdcUF5JC6da\nqbB0/LgxoWqxmKJrmqqSmJoiH4lQzGRYPHrUvF58fNzwyPV6mzT667hCIUK7d/P03/89AMO33GIG\n73I2y9KJE6iVCla3m9Z9+8yCZzmbBYuF2OgowV27jMJ0g4MWGHILLYODLD//fFN7pGSx4O/tNbp/\nJInMwoIh0by0hFat4u/rI7R7t/kk4+3sRK5p6Zi2izVDl+DgoOkCVkgk0KpVXK2t9F53HQAv/fM/\nNwmwudvaDEP5SKQp8K/O6dt9PjHFu40RXT2CC0o+GiU4NLTpebqmsXziBMGhIRyBANnlZWRFMbTu\nSyVjF6vrKB4PnQcO4O3spFoqUUgmjcGqwUEip04ZJusOB1a73VS1zMfjjPzrvxruWt3dREdGkCSJ\n5MwM3q4uug8eNPLjtacHgOTMDNVCgY4DB5AVhcipU2jV6pqupEo+T2j3bnIrK8iK0tQzLysK7tbW\nTYvaNpcLd1ubOURW78WvXyM4NER2cZFqsUhieppAfz+ttQLvzOOPc/WnP41aLpspn/DwMNHTp+m9\n9lqqxaLhOma1Ui0Wze+oeDyGiqlgWyJ2/IILRr07Z7MWTl3XSUxOGr6zO3ZQTKdJTE6aKQ61XKaS\ny9FxxRW0DA7SMjCAIxAwZZLdbW10X3MNrcPD+Ht7De9ZXaeUyZCLRPjll76EWiqx86ab8LS1ER8f\nJzU7S2ZhAVc4TKom4lZKpw0p5tFRssvLdFxxhdn5425tXVc6Obu4iL+3l9CuXWTm51/1LjrQ14da\nLmOxWknPzjbJKkiSZCiBDg2RmJhA1zQcfj++/n7mfvMbCrGY4bVb69l3t7aCJJFfWcFitaJWKqb9\nonnNWlun8OLdnojAL7hg5CORc1LhzMzPIzscWJ1OM/B62tupFgpkV1YoptO0X3EF/t5es/USjKeJ\nYiJBy86dZupHVhQUr9eUIc5HIpz41rdQfD6u/tSnaL/8cgbe9S4sNhtWpxN/Tw/u9nbTFCZ6+jTZ\nSMTUyknPz1PKZHAEgxQTiaaCaCGRwOZ2Y7XbsVitBIeGyC0vr3uD2AxPZydapWIUYx0OcjXjmUbs\nPh/d11zDwjPPYA8E6H3b26jm82SXl6kUCmSWlgAjBVWfS5DtdpHuEaxBBH7BBUEtl1ErlU1bOIup\nlDExa7NRTCTIRyKUalaK/h078LS3I2HsiCuFAlaHA0mSqOTzWKxWcisrplyCVq2Snp8neuoUxWQS\nVyjEmV/+klIyyeUf+xhdV1+Nvdbe6O3qouvKK8kuL5NbXkbxeCgmkwSHhmjft4/OAwcIDg6iuN0U\na4YspUyG2OioIfGg68bQWE3rBzCDfzmTMdU1z5W65r/V4QBdp5BIrNsW6gqF8O/YgVapENixA4DJ\nhx+mbd8+YiMjpjSzw+83cvyZDOVs1kj3NEg5Q7Oip2B7IQK/4IJwLiqcaqVCZn4ef18fM7/4BVq1\nirerC09nJ8Fdu7Da7aZJuM3ppJRKmVLHuUiEarFoavOkZmeJjY1hczoJDw/j7+tDttv59d/8DbLD\nwVV/8AfmxGohHjeGqNrbCe3eTbVUYv7ZZ82cfCmdxu7zYbFazYGq1uFh2i69lOzSEtGREZaOHUOS\nZdPPto5ksRjdSDXj9/VsFTfC3dFBOZtFLZeNFNSZM+ueF6jdEOtSFuMPPYTN6STQ10fkpZcMOWe/\nH6m2nrpV5OphLqvDgVoqCbXObYgI/IIt51xVOJPT07haW5l89FGsDge9116LIxAwDc3Vcpns8rK5\no68HZE1VqdRSHPZAgPjYGIrHQ+vwcFMH0fPf+Q6pM2cY/tCHaL3kEgBSZ87g7+sz9PK9XlP6uffQ\nIfSav24hHjfTSY0objehPXtwBoPomoZFlomcOkUuElnTE+/t6sLd1kZsdPSc2ybtXq8hrubxGIVe\nu73JWL2OzenEarfTdtllhIaHWTp2jHw8jq+3F8liITUzg+J2U8nn8fX0kFtZQVNVQ6BtlUa/ze0W\nap3bEBH4BVtOIZHAEQictYUzH42i6zqLR4/iDAbpfdvbsMiy0ZpZa+NMnTmDKxzG6nAY6RVNQ7bZ\nKMTj6EByagrF6SS8d+8aW0a1XObxe+7Boihc+yd/YrwvkTA6fWQZWVFMdy/F7cbf10fLwADOlhay\nKyukZmbWTP6C0S4ZPXUK/44dBAcHCe3aha6qRE+fJrO42KTI6fD7CQ4NkVlcJDU7u+nAlM3lagrE\n9kDAbAFdjaejA4vNRu9b3gLAye9+13hKsdmQLBayy8tmC6rV4SAfiRh/bpByrq9RpHu2HyLwC7ac\nzSZ11UqFxPQ0uaUlo0PH7zefDuq7el3XiU9MmJ625ZqCJhgBf+XkSbquuQZfT8+6SpzHvv51ktPT\n7PvoR2nfvx9NVckuLuLr7jakin0+EhMTuNvaDB3+Wv++rml07N9viLTNzxMbG6PQUNTVqlXj82o3\nNYvViqejg/DwMLLNRmxkhMzCgnkDkBWF0K5d2JxOoiMjZzU8t7lc6KqKJMvY/X5yy8t4OjtNeebV\n59q9XjqvugqA0X/7N9Oy0dPZaRZyi6kUgZ07jSlhDJ/fQixmXkfxepusHgXbAxH4BVtKOZdDttvX\n5L4biZ4+bXSaBIP4enqw2GzmcFS1UMBWm8y1OhxmX3z9hhAbHaWcy2FVFDN9s5pqscgv/sf/QLbb\nueFzn0OqDVG529qwWK0UkklzsMzh9xta+bVAXn/isPt8BIeG8Pf2Uq1N4cYnJlg8dszoq19aWtNy\n6QqHTXvEWG1qt36OKxymZXCQfDxO5NQpiqnUmicAuWa44unooJxOG0JtVqth7rJO901Lfz/e7m7s\ngQCLR4+SmpszpZhbBgYoZbPkVlbwtLdTSqeplkqmdLPp+Vv7vb+SWoTgjY8Y4BJsKZupcGZXVsjM\nz+Pp7CS8e7dhXFLb7de7dgBio6OEak5VYATk+vSqp7PT2KVvMB/w9D/8A9mFBa7+9KcJDg5SKRSo\n5PP4e3uN6diFBVqHh3GFw4ZIW8PgVv3GU8fqcBiDX11dZJaWsKbTaNUqhXicuV/9CkdLC5LF0jTV\nWw/okdOnKT39NO62NiN/XzNjUctlFp55Bk3TcAWD2NxuQ6dfUSimUriLRaqlEr6eHjILC7QMDJh1\njMbPqXsKdx88yOTDD5NbXjZsJCsVnMEgoaEhJh97jHCtLpFZXKSlv9+c6K3fVO0+H6VMZk26TPDm\nRQR+wZahViqopdKG7k5qtcr8008T2LkTb3u7EeiSyaZ0jt3no1IoUMpk8NZaJcv5PNmlJVovuQSH\n3092aYm2Sy9d9zPy0Si/uOceFJ+PG2rOcem5OXw9PQBER0Zw+Hx4OzsB42ZTbzkt53Ibtp9q1SrF\neJz2yy7DYrXi6+4mNjZGeO9edE1r2r1LkoQky7RKkukNrKsqvt7eJg38Su17qZUKrnDYsE8sFsks\nLFDO5chHo9h9PsrZLO72drMDqpGWgQHa9+9n8uGHmfh//48jf/mXjD/0EC2Dg1gdDkK7drF0/Dju\n9naK8ThqdzfOYNC4dkPgz0ejIvBvI0SqR7BlbJbbXzx61DAcqXniVotFs9gKRjpH8XqJj48THBgA\njN3z0vHjhoKlxYK15lG7kbXio3/xF5TTad5x9924w2GKqZTRI+92k4/FyC0v07pvn3l+XZa5/vn1\nwvJq0nNzeDo7zSlki9WKw++nEIsZxWKbzfyxWK0vD5TZbLTs3GnIPU9NNaWI6kJ0LTt3opZKJGt5\neJvTSc+hQ9i9XhS3m9mnnqJaKFDKZtd0CPl6emi77DIAzjz5JJqqGo5kJ0+ia5pRA5Flc9isPrNQ\nyefNm5XN5RKdPdsMEfgFW4KuaRSTyQ1NxrPLy6YWTX3XWkgkzF2mruumhHByepqWwUF0TSM+Po5V\nUUzJ4UIigb/B+rCRheee4+jXvkbL0BCH7rwTXdfJLCzg7eqiXMt3u2szAXUaUzsbBf56cXf1jtjd\n3m7k8c9B3ljxeMzUVWxkhHJDoJUVBW9XF+HhYdxtbSSmp4mNjBi1ErudtssvN2SUKxXmn3mmSWbB\nIsuEd++mZdcu5p95xtQfqhvY2JxOFLfb3NWX0mlD8qFB2K1uB7mRgY3gzYcI/IIt4WwtnGq5TOTk\nSby1NEM93VFMJs2BrLo9YGZhAWcohMVqJTE5iSMQwOZ2o5ZK2FwusouLZl9/I9nlZf7tv/030DR+\n62/+BllRyEcipg9vanYWV2urqcNfR1NVLFarWdxcrStULZXILi3hr03JNmKRZZyhELmVlXP6HUmS\nhKejg0B/P5n5eVNvv/G4KxzG19VFcGgIZzDI8okTlDMZssvLhC+9FG9XFzNPPEG+oTPH19dndPeo\nKpMPP4zi9SLV2jgzCwsoHg/utjbK2Sy6rpOPxXCFw02eAWKKd3shAr9gSzhbmic2NobN60XSdTzt\n7QCmTEM9zVPP78fHxgjt2kVqZsbU21c8npeHkHw+bLVirK7rFJNJoiMjvHj//cz/5jfseu972f2+\n96GpKvloFFc4TGJykkB/P5VcrskXoFosmjeh9Xb7uqaRnJ42rBBXSTLXcbe2mnLJ54rV4WjS2y+v\nSrNYHQ60ahVPeztdBw+ieDzIVivzv/kNNpfLvNnExsYMATa/n4HDhwGYfeopQ6LBasXd2mq4h0kS\npVSK4OAgas0ARlYUkCRTFkIE/u2FCPyC86aczW7YwllMJinEYti9XjydnWbPfTGZbJrsLWUySFYr\n1ULBCKKShN3nI7O0hFqtUkqnjcJsbagpMTlJ9NQpo/3S7+fxe+/F6nTy21/+MpIkkV1awtXaSnJ6\n2qgrOBxUcrmmwnOjCfl6gT85PY0rFDIlktdDslhwt7WRrQmkvRLcbW20DAyQmZ83HLYacv91u0d3\na6vhRLZ/v6G/4/ViraXDZEUhNjqKrqp0HzyI4vcz+dhj5KNR05wl0N9PKZMx/hv4/Xg6Oymn02QW\nF5t2/Va73UgnCfmGbYEI/ILzJru8bO7kG9E1jfjEBIrPh2SxNOXIi6mUmXbRdR2tWiV66hSVUoml\n48ep5PNkFhbIzM6SX1lB0zSKiQSuUAjZbsfb1UXrJZfg7+3lsb/4C/KRCO/47GcJ9PejlsuU0mkq\nhYLR8hgIUM5m13QbNQb+Sj7fFODT8/PIdvuGReRGnMEg5Wz2nL12G7Ha7QR37UKSZWI1t626UToY\nNxZHS4tZP9HKZVovuYT2yy8nu7yMVq2y8tJLVIpFdlx3HYVIhIXnnjMGs1Ipwyymv99QDJUkqoUC\nXYFgK1gAACAASURBVAcPsnziBLLdTimVMoO94nYLV65tggj8gvPibEbq2eVlw92qFqjrqOUykiSZ\n+fTs8jLZxUWWT5zAGQoxcOONtA4P4+vpwVfT17c6HHQcOICnowNnS4vZez/24IMc//rX6ThwgLf+\n8R8DkFlcRLbb0VXVbNssJpNr7B/rgb+cy5mdPYAxeVupmCJomyFJEr6eHtJzc6/gN9f8fk97O/4d\nO0jNzFDKZJrSP+62NkPiurXVkLrQNHzd3biCQfw1n9/s0hKttRbX2V/+kmqhYPrq2lwu/H19pKam\nqBaLhpZ/Tw/x0VFkRTFTPHafz9T0F7y5EYFfcF5slNuvSyTXVS4bnaka0zzZ5WXio6Mofj9Wp5Oe\ngwfNwaxiKoVareIIBMjMzRFY1c1TSCT48e23Y7HZeP83v2mobxaLRs69XDZli6FZ8qFxjRar1RCU\naygCa9XqugXks6F4PEgWy3nlyW1Op9H5o+skp6ao1LpsLLJsSC3E47jCYbOY7N+xg2xtInnXe95D\nxxVXgMXC2IMPkq+l1+pyDMHBQQqpFJVikWqxiLe7G5vbTSmbNe0elZpkteDNjwj8glfN/2fvzKPk\nuMtz/VRXV1fv6/TsqzSbNlsSljdhjI0XCITdB8OJIZDce8MNEMCGCxxwwEDYknsTIEBCEkPMvbFN\nIKwxYBuCZRtLtiVZ1jb73rP0vq9Vdf+o7mJGM6PNsi3J/Zzjc0Az6i5J01/96vve731VRVk3SD09\nPw+qigArTvvwezVPfGICpVjE3thIfGSE5h07VmzRFlMp1HIZVVGQHA4sJzxV/OIDHyAdCnHtnXfS\nfOmlAMQnJ9HK5RVB7bXFrOWKo0qxaPjzFKrh7rHRUUyiuGpJ6nRxt7WdsQ//iQiCgLutDU9XF+Gj\nR41QF3v1tF+7AaiKgihJOJubSc3MYJZlWrZvp3XXLuLDw8zu3QuiaHj21JbOSskk+VgMq9eLWi7j\n37CBxNQUxapnP4JQt294CXDKwq+qKnfeeSdve9vbuO2225iamlrx9e985zvccsst3HLLLXz9619/\n3i60zvlHPhrFFgisknAqpRLZpSUsLpeeiLWsd65WKnqIyfw8ks2m2xLMzaGqKg3LLBo0TTMKVPjo\nUSxOJ9GREcLHjhE+doxHv/xlDn3vezRdcgk7/uRPjG3f5PS0bphWjUyEddo81VzbcqFAKZ0mPjaG\ns7l51U3qTBAtFmw+35rpWWeKIxjE3dZGpVAgOjKCpijYAgFy0SiOpiZjmFyziC4kElg9Hgbf+EYA\nZp54gtjwMIlln1eb34+ztZXwsWMAWL1elHKZxq1bdQuJSqWu7nmJcMrC/9BDD1Eqlbjvvvu4/fbb\n+eIXv2h8bWZmhp/85Cfce++93H///Tz66KMcP378eb3gOucHmqYZcskTWa5wcVZ77DUKyaSenlXN\ny80sLZGZnydYdbesUcpkyC4ukg6FdO1+V5cRMm6WZfZ87nNIDgdv/Nd/xSSKZObnOfr972O22Sjn\ncpSyWUMjv5ZiJx+LUcrlCD35JJLdTmBgYEU76mxxNDVRSCSe8zKUxeGgks/j6ejA2dxMbGwMQRAM\ndU4xlUIplwHwdHaSDoUw2+10XXMNAKEnnqCUzSIIAouHDqGUStiqvkKi2UxmmarH3d6OxekkMjSE\n2WarF/6XAKf06nn66ae5pvrDtH37dg4fPmx8rbm5mX/6p39CrDkrVirIy7xI1uJrX/ta/cngIqCY\nTOr9+xP07ctP+4IgrJJCJqenEWUZd3s7mqoSOXoUyW5f0Y9XymVmn3gCwWxGlGVaL7/c2AhWSiV+\n8I53UEqneeN3v2u0eIrpNIH+flp37aKUTpOLRKhUzdkKiYSh11cVBbVcJjk9TcvOnVT8fhoGBtbV\n6Z8pgiDog9TpacOD6Gww22yGb77sctEwMKDn/2YyJKencbW0kA6F8HZ16W2cauCKzeejaccO5g8c\nMLT65moby93ejqYo2JubycdiyB4PpqqE1tPRoef2VgfbdS5uTnniz2QyOJfJ4ERRpFLtAUqShN/v\nR9M0vvSlL7F582Z61lmnr/H+97+foaGhFf89/PDDz/GPUeeFJrO4uGaQ+orT/rI8WtD76qnZWRqq\nBTE1N0elUNDtkautmFI2S2xkhEo+T/vllxue/TV+9ZGPMLd3L5f80R9xyW23AXrRjw0P03LZZZir\nEkxvVxcNg4M4GhtpuuQSXG1tuNra8PX00DA4iLe7G6vPZ1gfn0skux3Jbj+r0PUagiBgEkWj3y6Y\nTHg6Oghu3kz0+HHdZrk6qAVdkSPKMpVikcE3vAFUlfn9+5HsdpKTk/j7+sgsLuqSU01D9nr1oJtA\nwHhyqxQKeDs7ySwu1kPYL3JOWfidTifZZdIyVVU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w90gEwWQi0NdHpVhEUxQjAyHQ14do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\nKMTj9L761Wy46SYWDx5k7Je/RJRlw1zN6vHQfuWVhI8cwWyzUYjHsbh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4AAAL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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x115a587f0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.set_style('ticks')\n", | |
"\n", | |
"y = np.random.randn(20)\n", | |
"y_rep = np.random.randn(20, 20)\n", | |
"\n", | |
"ed.criticisms.ppc_density_plot(y, y_rep)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Iteration 1 [ 0%]: Loss = 11.152\n", | |
"Iteration 50 [ 10%]: Loss = 5.599\n", | |
"Iteration 100 [ 20%]: Loss = 5.539\n", | |
"Iteration 150 [ 30%]: Loss = 6.196\n", | |
"Iteration 200 [ 40%]: Loss = 6.045\n", | |
"Iteration 250 [ 50%]: Loss = 5.852\n", | |
"Iteration 300 [ 60%]: Loss = 5.668\n", | |
"Iteration 350 [ 70%]: Loss = 5.781\n", | |
"Iteration 400 [ 80%]: Loss = 6.353\n", | |
"Iteration 450 [ 90%]: Loss = 8.752\n", | |
"Iteration 500 [100%]: Loss = 6.039\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x1163ae710>" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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+fFmzZs3SCy+8oFWrVqmmpsavBQIAGq/OsN+6davS0tJUWVkpSVqzZo1SUlL0zjvvyLIs\nZWdn+71IAEDj1Bn2vXr1ktPpdD8+e/asHn/8cUnS2LFj9emnn/qvOgCAT9S5nv348eNVWFjofmxZ\nlhwOhyQpNDRUJSUldTbidDqVmZnZiDIBc9i5L0Hc6D62tQ3/avDmJQEB3/wxUFZWpg4dOtT5nuTk\nZCUnJ9d6rrCwUDExMQ1tHgDghQZfjTN48GAdP35cknT06FGNHDnS50UBAHyrwWG/fPlyOZ1OzZgx\nQ7dv39b48eP9URcAwIfqNY3To0cP7du3T5IUFRWlXbt2+bUoAIBvcVMVABiAsAcAAxD2AGCABl96\nCTQlrjkHfIORPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGICwBwADEPYA\nYADCHgAMQNgDgAEIewAwAGEPAAZgPXvgAexcSx/wNUb2AGAAwh4ADEDYA4ABCHsAMABhDwAGIOwB\nwACEPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAQh7ADCA1wuhTZkyRWFhYZKkHj16aM2aNT4rCgDgW16F\nfWVlpSzL0s6dO31dDwDAD7wK+9zcXJWXlyspKUlVVVVasmSJhg0b9sDXO51OZWZmel0kAKBxvAr7\nkJAQzZs3T/Hx8SooKNBLL72krKwstWlz/9MlJycrOTm51nOFhYWKiYnxpnkAQAN5FfZRUVHq3bu3\nHA6HoqKi1KlTJxUVFalbt26+rg8A4ANeXY3zhz/8QWvXrpUkXb9+XaWlpYqMjPRpYQAA3/FqZD99\n+nT96le/0qxZs+RwOJSRkfHAKRwAgP28Sujg4GBt2LDB17UAAPyEm6oAwACEPQAYgLAHAAMQ9gBg\nAMIeAAxA2AOAAQh7ADAAYQ8ABiDsAcAAhD0AGIAFbQDYLutYgS3txo3uY0u7dmBkDwAGIOwBwACE\nPQAYgLAHAAMQ9gBgAMIeAAxA2AOAAbjOHoCbXde7w/8Y2QOAAQh7ADAAYQ8ABiDsAcAAhD0AGICw\nBwADEPYAYACuswdgLDvvK2jqtfQZ2QOAAQh7ADAAYQ8ABiDsAcAAXn1BW1NTo/T0dOXl5Sk4OFi/\n/vWv1bt3b1/XBgDwEa9G9n/5y1/kcrm0d+9eLV26VGvXrvV1XQAAH/JqZH/ixAmNGTNGkjRs2DCd\nOXOmweeorq6WJF27ds2bEiRJXxZ5/97WJMDhkER/AC1JYaF3V77fzcy7GVpfXrVWWlqqsLAw9+PA\nwEBVVVWpTZv7n87pdCozM/O+xxISErwpAf+jQ/t2kqTXX51tcyUAmkpRUVGDps+9CvuwsDCVlZW5\nH9fU1Dww6CUpOTlZycnJtZ6rqKjQmTNnFBkZqcDAQG/KUExMjLKzs716b2tDX9RGf3yDvqitpfdH\ndXW1ioqKNGTIkAa9z6uwHzFihI4cOaIJEybo3//+twYMGNDgc4SEhGjkyJHeNF9Ljx49Gn2O1oK+\nqI3++AZ9UVtL7w9vLojxKuxjY2P1ySefaObMmbIsSxkZGd6cBgDQRLwK+4CAAL3++uu+rgUA4Cfc\nVAUABghMT09Pt7uIxhg1apTdJTQb9EVt9Mc36IvaTOwPh2VZlt1FAAD8i2kcADAAYQ8ABiDsAcAA\nhD0AGICwBwADEPYAYIBmH/Y1NTVauXKlZsyYocTERF2+fLnW8X379mnq1Kl6/vnndeTIEZuqbDp1\n9cf27dsVHx+v+Pj4B6402lrU1Rd3XzN//nzt2bPHhgqbVl398fHHH+v5559XfHy80tPT1dqvuq6r\nP7Zt26apU6dq2rRpOnz4sE1VNiGrmfvoo4+s5cuXW5ZlWSdPnrR++tOfuo998cUX1o9//GOrsrLS\nKi4udv/cmnnqj88++8yaMmWKVVVVZdXU1FgzZsywzp8/b1epfuepL+7asGGDFR8fb73zzjtNXV6T\n89QfJSUl1sSJE60vv/zSsizLeuutt9w/t1ae+uPrr7+2nnrqKauystK6deuW9fTTT9tVZpNp9iN7\nTxul5OTkaPjw4QoODlb79u3Vq1cv5ebm2lVqk/DUH127dtXbb7+twMBAORwOVVVVqW3btnaV6nd1\nbaKTlZUlh8Phfk1r56k/Tp48qQEDBmjdunV64YUXFBERofDwcLtKbRKe+uOhhx5S9+7dVV5ervLy\ncjn+fwOg1sy7rVKakKeNUkpLS9W+fXv3sdDQUJWWltpRZpPx1B9BQUEKDw+XZVl64403NHjwYEVF\nRdlYrX956ov8/Hx98MEH2rhxozZt2mRjlU3HU3/cvHlTx48f1/79+9WuXTslJCRo2LBhxv7/kKRu\n3bpp4sSJqq6u1sKFC+0qs8k0+7D3tFHKt4+VlZXVCv/WqK6NYyorK5WamqrQ0FCtWrXKjhKbjKe+\n2L9/v65fv67Zs2fr6tWrCgoK0ne/+12NHTvWrnL9zlN/dOrUSd///vcVGRkpSRo5cqTOnz/fqsPe\nU38cPXpUX3zxhXsTk3nz5mnEiBEaOnSoLbU2hWY/jTNixAgdPXpUku7ZKGXo0KE6ceKEKisrVVJS\nov/85z9ebaTSknjqD8uy9LOf/UwDBw7U66+/7vUOYC2Fp75YtmyZ3nvvPe3cuVNTpkzRnDlzWnXQ\nS577Izo6Wvn5+frqq69UVVWlU6dOqV+/fnaV2iQ89UfHjh0VEhKi4OBgtW3bVu3bt1dxcbFdpTaJ\nZr8QWk1NjdLT05Wfn+/eKOXo0aPq1auXYmJitG/fPu3du1eWZWnhwoUaP3683SX7laf+qKmp0ZIl\nSzRs2DD365csWaLhw4fbWLH/1PV/4y6n06mIiAjNmjXLxmr9r67++POf/6zf/e53kqS4uDgtWLDA\n5or9q67+2Lhxo/72t78pICBAI0aM0LJly1r13H2zD3sAQOM1+2kcAEDjEfYAYADCHgAMQNgDgAEI\newAwAGEPAAYg7AHAAP8HobyIdfukY2cAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11613f908>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"ed.set_seed(42)\n", | |
"\n", | |
"# DATA\n", | |
"x_data = np.array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1])\n", | |
"\n", | |
"# MODEL\n", | |
"p = Beta(a=1.0, b=1.0)\n", | |
"x = Bernoulli(p=tf.ones(10) * p)\n", | |
"\n", | |
"# INFERENCE\n", | |
"qp_a = tf.nn.softplus(tf.Variable(tf.random_normal([])))\n", | |
"qp_b = tf.nn.softplus(tf.Variable(tf.random_normal([])))\n", | |
"qp = Beta(a=qp_a, b=qp_b)\n", | |
"\n", | |
"inference = ed.KLqp({p: qp}, data={x: x_data})\n", | |
"inference.run(n_iter=500)\n", | |
"\n", | |
"# CRITICISM\n", | |
"x_post = ed.copy(x, {p: qp})\n", | |
"\n", | |
"T = lambda xs, zs: tf.reduce_mean(xs[x_post])\n", | |
"\n", | |
"ppc_stats = ed.ppc(lambda xs, zs: tf.reduce_mean(tf.cast(xs[x_post], tf.float32)),\n", | |
" data={x_post: x_data})\n", | |
"\n", | |
"ppc_stat_hist_plot(ppc_stats[1][1], ppc_stats[0], stat_name=r'$T \\equiv mean$',\n", | |
" bins=10)" | |
] | |
} | |
], | |
"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": "3.4.5" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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