diff --git a/notebooks/01a-instructor-probability-simulation.ipynb b/notebooks/01a-instructor-probability-simulation.ipynb index c06fe95..5245851 100644 --- a/notebooks/01a-instructor-probability-simulation.ipynb +++ b/notebooks/01a-instructor-probability-simulation.ipynb @@ -95,7 +95,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -127,7 +127,7 @@ { "data": { "text/plain": [ - "'Number of clicks = 502'" + "'Number of clicks = 500'" ] }, "execution_count": 3, @@ -157,7 +157,7 @@ { "data": { "text/plain": [ - "'Proportion who clicked = 0.502'" + "'Proportion who clicked = 0.5'" ] }, "execution_count": 4, @@ -217,8 +217,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of clicks = 712\n", - "Proportion who clicked = 0.712\n" + "Number of clicks = 708\n", + "Proportion who clicked = 0.708\n" ] } ], @@ -407,7 +407,7 @@ { "data": { "text/plain": [ - "0.8558" + "0.8515" ] }, "execution_count": 9, @@ -504,7 +504,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -606,7 +606,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -718,7 +718,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -802,7 +802,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -863,7 +863,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1242,5 +1242,5 @@ "toc-autonumbering": true }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/02-instructor-parameter-estimation.ipynb b/notebooks/02-instructor-parameter-estimation.ipynb index a5a605a..64c5865 100644 --- a/notebooks/02-instructor-parameter-estimation.ipynb +++ b/notebooks/02-instructor-parameter-estimation.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -139,7 +139,7 @@ " x = np.linspace(0, 1, 100)\n", " \n", " # Write out equation for uniform prior\n", - " prior = 1\n", + " prior = np.ones(len(x))\n", " \n", " # Write out equation for posterior, which is likelihood * prior.\n", " posterior = (x**n_successes) * ((1-x)**(N-n_successes)) * prior\n", @@ -154,12 +154,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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CNOquDsi0Th+Q4hfCT1wuN0fOdlJemEqW3Gxl2aqK0xkak2mdviDFL4SfnLs6wODoDHeskzd1fUGmdfqOV1eSKKXKgOeBdGAAeEJr3XiD/b4IfBuwAG5gj9a6x3dxhQgd79V0kJwQxbrSDKOjhIWF0zrv27rS6Dghzdsj/meAp7XWZcDTwLPX76CU2gR8B7hLa10J7ABGfJRTiJDSPzxF3ZUBbqvOIcImv1j7SlWxTOv0hZu+IpVSmcAGYL9n035gg1LKft2ufwN8X2vdDaC1HtFaT/syrBCh4sjZTrAgN1vxsaqi+Wmd9S0yrXM5vDkUyQc6tNZOAM+/Oz3bFyoHViml3ldKnVFKfUspJUsQCtNxOF0cPdvJ2uIM0pNjjI4TVkrykomOtHG+edDoKCHNl6tFRQDVwF1AFPAW0Ar8zNtPkJ6esOQvbreb71J4GXNwOlrbwejkHA/fUeKTvKEwZl/7rDFXl2ZQ3zIcdv9fAjkeb4q/DchVStm01k6llA3I8WxfqAV4RWs9A8wopV4HtrCI4h8YGMflWvwcXbs9kb6+sUU/LpTJmIPXvx25TEZyDPlpscvOGypj9qWbjbksN5lTF3u40NBDZmp4TJNd6vNstVqWdMB801M9WuteoBbY69m0F6jRWvddt+uLwN1KKYtSKhLYDZxddCIhQljXwAT1rcPcvi4Hq1XOdPrDtWmd55vkdM9SeTvd4Elgn1KqAdjn+Ril1AHPbB6AfwV6gYvM/6C4APzYt3GFCG7v1XRis1rYUS1v6vpLZmosGckxXJDiXzKvzvFrreuBrTfYfv+Cv7uA/+j5I4TpzM45OV7XxUZlJzk+yug4YctisVBZlMaJiz04nC6ZLrsE8n9MCB85eamXyRmHLL8cABVF6UzPOrnSIZcKLYUUvxA+crimg+z0OMryU4yOEvbWrEzFarFwQaZ1LokUvxA+0NI9RlPXKHesz8VikTd1/S0uJoJVuUmcvyrFvxRS/EL4wOGadqIirdxaucLoKKZRWZhGS/cYo5OzRkcJOVL8QizT5PQcJy70cEt5FnExkUbHMY2KVWm4gUtyV65Fk+IXYpmOn+9m1uFi1/o8o6OYStGKJOKiI+Q8/xJI8QuxDG63m/dqOijKTmLlivBaQiDYWa0W1qxM5WLzoNyVa5Gk+IVYhvrWYboGJrlzg0zhNEJFURqDozN0D04aHSWkSPELsQyHazqIj4lg8+pMo6OYUrln+Qa5indxpPiFWKKhsRlqGvrYUZ1NVKTN6DimlJkSiz0lhovyBu+iSPELsURHajtwudxypa7BKgrTuNQ6hMPpMjpKyJDiF2IJHE4XR852UrkqPWyWBg5VFUVpzMw6udo5anSUkCHFL8QSnGnoY2R8Vt7UDQJrVqZisch5/sWQ4hdiCQ6d6SAjOYaqVelGRzG9uJhIirKTuCjz+b0mxS/EIrX3jtPQNsyuDblys5UgUVGYxtWuUSan54yOEhKk+IVYpEM1HURGWNkpN1sJGhVFabjdcKlFZvd4Q4pfiEWYnJ7jg/NdbFmTSUKsrMsTLFblJBEdZZNpnV6S4hdiEY6d62J2zsWejflGRxELRNisrM5PkXV7vCTFL4SXXG43h850UJKXLOvyBKHywjR6h6boH54yOkrQk+IXwkt1VwboHZ5iz0ZZhTMYfbx8gxz135QUvxBeevd0OykJUWwosxsdRdxATnocKQlRXJDz/DclxS+EF7oGJjjfNMiu9blE2OTbJhhZLJb55RuaB3G5ZJnmzyKvYCG8cOh0BxE2C7evkyt1g1l5URoT0w5ae8eMjhLUpPiFuInJ6TmO1XWxeXUWSfFRRscRn6G8UJZp9oYUvxA3cfRcFzNzTu7eLFM4g11yfBR59niZz38TUvxCfAany8XBj9opy0+RKZwhorwwjcb2YWbmnEZHCVpS/EJ8htrGfgZGp+VoP4RUFKXhcLppbBs2OkrQkuIX4jO8c6qNjOQY1pVkGB1FeKksP4UIm0Xm838GKX4hPkVz9ygN7SPs2ZQvq3CGkOhIG6V5KVxokvP8n0aKX4hP8c6pNmKibOyszjY6ilik8sJU2vvGGRmfMTpKUJLiF+IGBkenOXmplx3V2cRGRxgdRyxSZdH8DXJkds+NefWKVkqVAc8D6cAA8ITWuvFT9lVADfBDrfXf+iqoEIF08HQ7bjfcvUne1A1F+VkJJMRGcr5pkG2VK4yOE3S8PeJ/Bnhaa10GPA08e6OdlFI2z397zTfxhAi8qRkHR2o72LTaTkZKrNFxxBJYLRbKC1O52DyI2y3LN1zvpsWvlMoENgD7PZv2AxuUUjdaqervgF8DDT5LKESAvX+2k6kZJ/dsKTA6iliGisI0RiZm6eibMDpK0PHmVE8+0KG1dgJorZ1KqU7P9r5rOymlqoF7gF3At5cSJj09YSkPA8BuN9/FNTJm33M4XRyq6aCqOIMt1cGxLo88z0uzc2MBP3mznpb+CdZXBP8b9IF8nn3yrpVSKhJ4Dviq5wfDkj7PwMD4klbVs9sT6esz16JMMmb/OHGxm76hKf7d7tKg+P8rz/PyZKfH8WFdF7eWZ/nk8/nLUsdstVqWdMDszTn+NiDXc/7+2nn8HM/2a7KBYuCAUqoZ+AbwNaXUjxadSAiDuN1u3vqwlez0OKqK042OI3ygvDCNhrZh5hyyfMNCNy1+rXUvUAvs9WzaC9RorfsW7NOqtc7QWhdqrQuBHwDPaa2/7ofMQvjFheZBWnvGuWdLAVaLXLAVDioK05h1uGhsHzE6SlDxdlbPk8A+pVQDsM/zMUqpA0qpTf4KJ0QgHfhdC6mJ0WyX6X9hQxWkYLNaZJnm63h1jl9rXQ9svcH2+z9l/+8sL5YQgXWlY4T61mEev7NE7rAVRmKjIyjJTeZ80yCP7TI6TfCQV7gQwIETLcTHRHDbuhyjowgfq1yVRluvLN+wkBS/ML2O/glqGvvZvTGPmChZniHcXFu+4byc7vmYFL8wvTdPtBAVaWWPLM8QlvKzEkiKi5Tz/AtI8QtT6xue4sOLPdy+NpeE2Eij4wg/sFosVBSlcb5pEJcs3wBI8QuT+83vWrBYLNy7VZZnCGeVRemMT83R0m2ui+E+jRS/MK3+kSmO13Vx29psUhOjjY4j/KiiKA2Q8/zXSPEL03rzRCsA99+y0uAkwt+S4qNYmZXIhasDRkcJClL8wpQGR6c5eq6TndXZpCXFGB1HBEDlqjSudI4yNeMwOorhpPiFKb35YStutxztm0llURpOl5tLLXJXLil+YTpDYzMcqe1kW+UKudGKiRTnJhMTZaNOTvdI8Qvz+fUHzbjdbh7aXmh0FBFAETYrFYVp1F0dMP1duaT4han0DU/x/tlOblubI0f7JlRVnM7g6Awd/ea+K5cUvzCVfzvehNVq4XNytG9KVavml284d8Xcp3uk+IVpdA1M8MH5bnatz5V5+yaVmhhNQWaCFL/RAYQIlNePNREVYZOZPCZXVZzO5fYRJqfnjI5iGCl+YQot3WOcvNTLnk15JMVHGR1HGKi6OB2X282FZvNO65TiF6bwynuXSYiN5L6tcrRvdsU5ycTHRHDuSr/RUQwjxS/C3vmmAS40D/Hg9kLiYmS9fbOzWi1Urkqn7sqAaVfrlOIXYc3lcvPy4SvYU2LYtSHX6DgiSFSvSmd00ryrdUrxi7D2uwvdtPWO8/nbiuVeuuJjFavSsGDeaZ3ynSDC1uyck18dvUrhikQ2r8k0Oo4IIklxUazKTaL2sjnP80vxi7D19slWBkdn+OKuEqwWi9FxRJBZV5JBS/cYg6PTRkcJOCl+EZYGR6f5zYkWNio7q1emGh1HBKH1pXYAUx71S/GLsPTKe1dwu+FLu0qMjiKCVHZ6HFmpsdQ2SvELEfIa24c5cbGHe7cUyEJs4lNZLBbWl9q51DJkupuzSPGLsOJyuXnxnUZSE6NlaQZxU+tKM3C63KZbo1+KX4SVI2c7aekZ47FdxURH2YyOI4JcSW4yCbGRpjvdI8UvwsbI+AyvvHeFNStT2bomy+g4IgRYrRbWlqRz7soADqfL6DgBI8UvwsZLhy4z53DylbvLsMj0TeGl9aV2JmccNLYNGx0lYKT4RVi40DTIiYs93H/LSrLT442OI0JIRWEakRFWzpjodI9XK1YppcqA54F0YAB4QmvdeN0+3wYeBxyeP9/UWr/t27hC/L45h5MXfqvJSo3lgW3yhq5YnOgoG5VFaZxp6GPvnlJTXOzn7RH/M8DTWusy4Gng2RvscxLYrLVeC/wx8JJSSubSCb97/VgzvUNT/OE9isgIeUNXLN6m1ZkMjc1wpWPE6CgBcdPiV0plAhuA/Z5N+4ENSin7wv201m9rrSc9H54DLMz/hiCE3zR1jfLmhy3srM6mvDDN6DgiRK0rySDCZuVUfa/RUQLCmyP+fKBDa+0E8Py707P90zwBXNFaty8/ohA3Nudw8uPfXCIlIZov3VlqdBwRwmKjI6halcZH9b2mWKPf53elUErdDvw9cNdiH5uenrDkr2u3Jy75saHK7GP+2YGLdPZP8J2v3cLK/PBdj8fsz3Og3LllJf/0i9MMTMxRXhT4kxWBHLM3xd8G5CqlbFprp1LKBuR4tn+CUmob8HPgYa21XmyYgYFxXK7F/7S12xPp6zPXDRXMPuarnaO8cqiRHdXZFKTHhe3/C7M/z4G0KjOeCJuVd040Y08I7H2Zlzpmq9WypAPmm57q0Vr3ArXAXs+mvUCN1rpv4X5Kqc3AS8CjWuszi04ihJemZhz86I0LpCZG8/idsgib8A0zne7xdlbPk8A+pVQDsM/zMUqpA0qpTZ59fgjEAs8qpWo9f6p8nliY3osHG+gbnuLrD1YQFxNpdBwRRjavzmR4fDbsZ/d4dY5fa10PbL3B9vsX/H2zD3MJcUMfXuzheF03D24vpCw/xeg4IsysvTa751IvpXnh+/qSK3dFyOgZnORnb2uKc5N4aEeh0XFEGIqNjmBtcTon63txusJ37R4pfhES5hxOvvezU4Cbrz9Ygc0qL13hH9srVzA6Mcv5q4NGR/Eb+e4RIeHFg41cbhvmTx8oxy43VxF+VFWcTkJsJMfruoyO4jdS/CLoHT3XyZHaTh69s5T1ZfabP0CIZYiwWbmlPIvay/2MT80ZHccvpPhFUGvpHuOFtxtYszKVr9y72ug4wiRurcrG4XRz6lKP0VH8QopfBK2hsRmeevUciXGR/NnDFdhs8nIVgVGQlUCePZ7j57uNjuIX8p0kgtLMrJOnXjnH5IyDv360mqS4wF5JKczNYrGwvTKbq52jdA1MGB3H56T4RdBxudz86I0LtPaO8eRDFRRkmW+tGmG8bRVZWC0WPgjDo34pfhFU3G43Lx26TE1jP4/vLmVtSYbRkYRJJSdEU7kqjWN1XWF3P14pfhFU3vigmXc+amPPxjz2bMwzOo4wuTs35DIyPsvpTy5NFvKk+EXQOPhRG68dbWJ75Qoe31MqN0wXhqtclU5maiwHT//eYsQhTYpfBIXjdV28eLCR9aUZfPX+1aa476kIflaLhd0b8rjSMUpz96jRcXxGil8Y7v2znfyf31xizcpUnnxYlmMQweXWqmyiI228+1H43FBQvsOEoQ6daeenb9ZTUZTGXz9aLTdLF0EnLiaC7VUr+PBSD6MTs0bH8QkpfmGYtz5s5ee/bWBdSQb7vlBNVKSUvghOuzfk4XC6OXK20+goPiHFLwLO5XLz4jsN/N/Dl9m0OpO/+INKIiPkpSiCV05GPBWFqRw6087snNPoOMsm320ioGbmnDz9qzoOnm7n7s35PPlQBRGyFIMIAQ9sK2RkfJYjtaF/1C/fcSJgBkam+e+/OENtYz9795Ty+O5SrFaZvSNCw+qVqawuSOE3J1qYCfGjfil+ERAXmwf5Lz89RffgJH/5+Sru2pRvdCQhFu2RnasYnZjl8JkOo6Msi1f33BViqVwuNwdOtPCro1fJTo/nP/xBJdnp8UbHEmJJyvJTqChK48CJFu5Yn0NMVGhWqBzxC7/pH57if7x4hl++f5XNqzP51hMbpfRFyHtkZxHjU3O8ezp05/WH5o8rEdTcbjfH6rrYf7ARgD95YA3bK1fIEgwiLBTnJFNdnM5z9UoZAAAJr0lEQVSbJ1rZUZ1DcnzoLRkuR/zCpzr7J/jH/TX85EA9K7MS+e6fbOHWqmwpfRFWvrirhFmHk/0HG4yOsiRyxC98YmrGwYETLbz1YSsxUTaeuFdx29ocWXNHhKWcjHg+t62Q1441sa2iP+SWD5fiF8vicLp4r6aDNz5oZmxyju2VK/jirhKSQvDXXyEW4/5tKzlZ38sLv9WU5acQGx06dRo6SUVQmXM4OVbXzZsnWugfmWZ1QQqP7SqhKDvJ6GhCBESEzcof3beaf3jhNL98/ypfvqvM6Ehek+IXizI2Ocv7Zzt556N2RidmKcpO5Il7FBVFaXIeX5hOSW4yd27I493T7aj8FDatzjQ6klek+MVNud1uGttHOFLbwan6PhxOF5VFadx3y0pWF6RI4QtTe2xXMc09ozz364ukJkZTnJtsdKSbkuIXN+R2u+nom+DDSz2cvNRD3/A0sdE2bl+bw+3rc8izJxgdUYigEBVpY98XqvmvP/uIp149x7ee2IQ9JdboWJ9Jil98bM7hpKF9hLOX+zl3eYDe4SmsFgtrClN5cHsRm1dnEh0lSycLcb2kuCi+8dha/tsLp/nBy2f5my+uJSM5eMtfit/ExqfmaO4e5XL7CA1tw1zuGMXhdBFhs1JemMo9W/LZqDJlho4QXshOj+cvP1/FU6+e47s//Yg/e6iCiqI0o2PdkFfFr5QqA54H0oEB4AmtdeN1+9iAp4B7ATfwPa31v/g2rliKOYeT3uFpugcmaO+boKNvnNaecXqHpwCwWKAgK5HdG3NRBamsKUiVI3shlkAVpPKf/v1m/vlXdfzPl2p5ZGcR925dGXT3m/D2iP8Z4Gmt9c+VUl8BngXuvG6fLwMlQCnzPyBqlFIHtdbNvgorPsnpcjEx7WBiao6xyTlGJmYZnZhlaGyGwbFpBkdn6B+ZYmh0BrfnMRbAnhpLXmYCO9dmU5SdROGKJOJi5Jc/IXwhKy2Ob/3hJp5/q55fHW3i0JkOdm3I5Y71uSTFBcdvzzf9bldKZQIbgLs8m/YD/6yUsmut+xbs+iXgOa21C+hTSr0GPAb8o48zf4LD6eLE+S4GhyaX/bncbvfNd/q9x4D7Wq26+bhgcYPL8/nc7vnP7XK7cXu2u1xuXO751SudLhdOlxuHc/7vDocbh9PFrMPJnMPF7JyLmTkns3NOpmadTM04mJlzMjntuGEmm9VCamI0aYnRqPwUslLjyEyNJSstjpyMeKLlFodC+FV0lI2vPVjO9qoV/PZUG68dbeKN483k2uPJsyeQa48nNjqCSJuVuOgIdqcFdvFCbw7z8oEOrbUTQGvtVEp1erYvLP4CoGXBx62efbyWnr74mSIfnOvkH54/tejHBSOLZf6ikAibhahIG5ERNqIirERH2YiJiiAxIZqsmAjioiOJi4kgIS6KxLhIEmIjSU6IJiVx/k9SfDS2ML3Bid2eaHSEgJMxh67MzCR2bSmkrWeMd0+10tQ5Sn3rEB+c7/7EfvaMBNarwF0DEFS/3w8MjONyLe6ouzQ7kWf/bje9fWO+CeHFnPTr91j4EIvF8vF/t1jm/2EBrBYLFsv8f7daLVgtYLVasHn+zG+zeD0n3m5PpO8GY3ZMzzE4PefV5wg1nzbmcCZjDg8xVnhga8HHH09OzzE968ThdGGxWCgvzVzSmK1Wy5IOmL0p/jYgVyll8xzt24Acz/aFWoGVwLXD7+t/A/CbHHsCkSz+NI0QQhghLiaSuJhIw77+Td9q1lr3ArXAXs+mvUDNdef3AV4GvqaUsiql7MAjwKu+DCuEEGL5vJ1j9CSwTynVAOzzfIxS6oBSapNnnxeAq0AjcAL4rtb6qo/zCiGEWCavzvFrreuBrTfYfv+CvzuBP/ddNCGEEP4QXFcVCCGE8DspfiGEMBkpfiGEMJlgmcdvg/k5qUu1nMeGKhmzOciYzWEpY17wmEVdjm9ZyjIFfrADOGp0CCGECFE7gWPe7hwsxR8NbAa6AKfBWYQQIlTYgGzmL5yd8fZBwVL8QgghAkTe3BVCCJOR4hdCCJOR4hdCCJOR4hdCCJOR4hdCCJOR4hdCCJOR4hdCCJMJliUbbkopVQY8D6QDA8ATWuvG6/axAU8B9zJ/3/Pvaa3/JdBZfcXLMX8beBxweP58U2v9dqCz+oo3Y16wrwJqgB9qrf82cCl9y9sxK6W+CHyb+bt/uoE9WuueQGb1FS9f25nAT5i/d3cUcAj4K621I8Bxl00p9X3gC0AhUKW1Pn+DfQLWX6F0xP8M8LTWugx4Gnj2Bvt8GSgBSoFtwHeUUoUBS+h73oz5JLBZa70W+GPgJaVUbAAz+po3Y772TfIs8FoAs/nLTcfsueHRd4C7tNaVzC9zMhLIkD7mzfP8TeCS1roaqAI2Ap8PXESfeg24jc++HW3A+iskit/zk38DsN+zaT+wwXOLx4W+BDyntXZ5bg35GvBY4JL6jrdj1lq/rbWe9Hx4jvmjwfSABfWhRTzPAH8H/BpoCFA8v1jEmP8G+L7WuhtAaz2itZ4OXFLfWcSY3UCiUsrK/LIuUUBHwIL6kNb6mNb6+vuUXy9g/RUSxc/8r3odnrt8XbvbV6dn+0LX3+C99Qb7hApvx7zQE8AVrXV7APL5g1djVkpVA/cA/yvgCX3P2+e5HFillHpfKXVGKfUtpVSoLmHp7Zj/Hihjfg2vbuBtrfXxQAYNsID1V6gUv7gJpdTtzH+j7DU6iz8ppSKB54AnrxWHSUQA1cBdwO3AfcAfGprI/x5j/rfYbCAXuE0p9aixkcJDqBR/G5DrOa977fxujmf7Qq3AygUfF9xgn1Dh7ZhRSm0Dfg48orXWAU3pW96MORsoBg4opZqBbwBfU0r9KLBRfcbb57kFeEVrPaO1HgNeB7YENKnveDvmfcAvPKc+Rpgf866AJg2sgPVXSBS/1roXqOX/H83uBWo858EWepn5ErB6zhc+ArwauKS+4+2YlVKbgZeAR7XWZwKb0re8GbPWulVrnaG1LtRaFwI/YP686NcDHtgHFvHafhG4Wyll8fzWsxs4G7ikvrOIMTcxP8MFpVQUsAf4vdkwYSRg/RUSxe/xJLBPKdXA/JHAkwBKqQOeGQ8ALwBXgUbgBPBdrfVVI8L6iDdj/iEQCzyrlKr1/KkyJq5PeDPmcOPNmP8V6AUuMl+aF4AfG5DVV7wZ8zeAnUqpOubH3MD8ab6Qo5R6SinVDuQBB5VSFzzbDekvWY9fCCFMJpSO+IUQQviAFL8QQpiMFL8QQpiMFL8QQpiMFL8QQpiMFL8QQpiMFL8QQpiMFL8QQpjM/wMujhtd69RgugAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -183,12 +183,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -268,7 +268,7 @@ " likelihood = x**n_successes*(1-x)**(N-n_successes) \n", " \n", " # Write out equation for posterior given uniform prior\n", - " prior_uniform = 1 \n", + " prior_uniform = np.ones(len(x))\n", " posterior_uniform = likelihood * prior_uniform\n", " posterior_uniform /= np.max(posterior_uniform)\n", " plt.plot(x, posterior_uniform, label='Uniform prior')\n", @@ -289,7 +289,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/notebooks/03-instructor-bayesian-estimation.ipynb b/notebooks/03-instructor-bayesian-estimation.ipynb index 9b87777..5c07ca0 100644 --- a/notebooks/03-instructor-bayesian-estimation.ipynb +++ b/notebooks/03-instructor-bayesian-estimation.ipynb @@ -9,9 +9,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.configdefaults): install mkl with `conda install mkl-service`: No module named 'mkl'\n" + ] + }, { "data": { "application/javascript": [ @@ -3210,7 +3217,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -3241,61 +3248,61 @@ " \n", " \n", " \n", - " 943\n", - " control\n", - " 0\n", + " 2131\n", + " test\n", " 0\n", + " 1\n", " \n", " \n", - " 1810\n", + " 303\n", " control\n", - " 0\n", + " 1\n", " 0\n", " \n", " \n", - " 2525\n", - " test\n", + " 811\n", + " control\n", + " 0\n", " 0\n", - " 1\n", " \n", " \n", - " 2568\n", - " test\n", + " 152\n", + " control\n", + " 0\n", " 0\n", - " 1\n", " \n", " \n", - " 1715\n", + " 1836\n", " control\n", " 1\n", " 0\n", " \n", " \n", - " 2245\n", - " test\n", + " 665\n", + " control\n", + " 0\n", " 0\n", + " \n", + " \n", + " 818\n", + " control\n", " 1\n", + " 0\n", " \n", " \n", - " 1379\n", + " 242\n", " control\n", " 0\n", " 0\n", " \n", " \n", - " 2132\n", + " 2751\n", " test\n", " 0\n", " 1\n", " \n", " \n", - " 1059\n", - " control\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 2790\n", + " 2595\n", " test\n", " 0\n", " 1\n", @@ -3306,19 +3313,19 @@ ], "text/plain": [ " group clicks group_enc\n", - "943 control 0 0\n", - "1810 control 0 0\n", - "2525 test 0 1\n", - "2568 test 0 1\n", - "1715 control 1 0\n", - "2245 test 0 1\n", - "1379 control 0 0\n", - "2132 test 0 1\n", - "1059 control 0 0\n", - "2790 test 0 1" + "2131 test 0 1\n", + "303 control 1 0\n", + "811 control 0 0\n", + "152 control 0 0\n", + "1836 control 1 0\n", + "665 control 0 0\n", + "818 control 1 0\n", + "242 control 0 0\n", + "2751 test 0 1\n", + "2595 test 0 1" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -3331,6 +3338,72 @@ "ctr.sample(10)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
clicksgroup_enc
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control0.140500.0
test0.191251.0
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" + ], + "text/plain": [ + " clicks group_enc\n", + "group \n", + "control 0.14050 0.0\n", + "test 0.19125 1.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ctr.groupby(\"group\").mean()" + ] + }, { "cell_type": "markdown", "metadata": { @@ -3432,7 +3505,7 @@ ], "source": [ "with model1_bernoulli:\n", - " samples_bernoulli = pm.sample(2000, tuning=1000)" + " samples_bernoulli = pm.sample(2000, tune=1000)" ] }, { @@ -3454,7 +3527,7 @@ ], "source": [ "with model1_binomial:\n", - " samples_binomial = pm.sample(2000, tuning=1000)" + " samples_binomial = pm.sample(2000, tune=1000)" ] }, { @@ -4297,17 +4370,17 @@ "source": [ "players = (\n", " pd.read_csv(\"../data/baseballdb/core/Batting.csv\")\n", - " .clean_names()\n", + " .clean_names() # FYI: pyjanitor\n", " .query(\"yearid == 2016\")\n", - " .select_columns(['playerid', 'ab', 'h'])\n", + " .select_columns(['playerid', 'ab', 'h']) # FYI: pyjanitor\n", " .groupby('playerid').sum()\n", ")\n", "\n", "salaries = (\n", " pd.read_csv(\"../data/baseballdb/core/Salaries.csv\")\n", - " .clean_names()\n", + " .clean_names() # FYI: pyjanitor\n", " .query(\"yearid == 2016\")\n", - " .select_columns([\"playerid\", \"salary\"])\n", + " .select_columns([\"playerid\", \"salary\"]) # FYI: pyjanitor\n", " .groupby('playerid').mean()\n", ")\n", "\n", @@ -4600,237 +4673,6 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hands-on: Parameter estimation II -- the mean of a population" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this exercise, you'll calculate the posterior mean beak depth of Galapagos finches in a given species. First you'll load the data and subset wrt species:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import and view head of data\n", - "df_12 = pd.read_csv('../data/finch_beaks_2012.csv')\n", - "df_fortis = df_12.loc[df_12['species'] == 'fortis']\n", - "df_scandens = df_12.loc[df_12['species'] == 'scandens']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To specify the full probabilty model, you need\n", - "- a likelihood function for the data &\n", - "- priors for all unknowns.\n", - "\n", - "What is the likelihood here? Let's plot the measurements below and see that they look approximately Gaussian/normal so you'll use a normal likelihood $y_i\\sim \\mathcal{N}(\\mu, \\sigma^2)$. The unknowns here are the mean $\\mu$ and standard deviation $\\sigma$ and we'll use weakly informative priors on both\n", - "- a normal prior for $\\mu$ with mean $10$ and standard deviation $5$;\n", - "- a uniform prior for $\\sigma$ bounded between $0$ and $10$.\n", - "\n", - "We can discuss biological reasons for these priors also but you can also test that the posteriors are relatively robust to the choice of prior here due to the amount of data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sns.distplot(df_fortis['blength']);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with pm.Model() as model:\n", - " # Prior for mean & standard deviation\n", - " μ_1 = pm.Normal('μ_1', mu=10, sd=5)\n", - " σ_1 = pm.Lognormal('σ_1', 0, 10)\n", - " # Gaussian Likelihood\n", - " y_1 = pm.Normal('y_1', mu=μ_1, sd=σ_1, observed=df_fortis['blength'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# bust it out & sample\n", - "with model:\n", - " samples = pm.sample(2000, njobs=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "az.plot_posterior(samples);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Bayesian Hypothesis testing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bayesian Hypothesis testing I: A/B tests on click through rates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Assume we have a website and want to redesign the layout (*A*) and test whether the new layout (*B*) results in a higher click through rate. When people come to our website we randomly show them layout *A* or *B* and see how many people click through for each. First let's generate the data we need:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# click-through rates\n", - "p_a = 0.15\n", - "p_b = 0.20\n", - "N = 1000\n", - "n_successes_a = np.sum(np.random.uniform(size=N) <= p_a)\n", - "n_successes_b = np.sum(np.random.uniform(size=N) <= p_b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once again, we need to specify our models for $p_a$ and $p_b$. Each will be the same as the CTR example above\n", - "- Binomial likelihoods\n", - "- uniform priors on $p_a$ and $_p$.\n", - "\n", - "We also want to calculate the posterior of the difference $p_a-p_b$ and we do so using `pm.Deterministic()`, which specifies a deterministic random variable, i.e., one that is completely determined by the values it references, in the case $p_a$ & $p_b$.\n", - "\n", - "We'll now build the model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with pm.Model() as Model:\n", - " # Prior on p\n", - " prob_a = pm.Uniform('p_a')\n", - " prob_b = pm.Uniform('p_b')\n", - " # Binomial Likelihood\n", - " y_a = pm.Binomial('y_a', n=N, p=prob_a, observed=n_successes_a)\n", - " y_b = pm.Binomial('y_b', n=N, p=prob_b, observed=n_successes_b)\n", - " diff_clicks = pm.Deterministic('diff_clicks', prob_a-prob_b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample from the posterior and plot them:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with Model:\n", - " samples = pm.sample(2000, njobs=1)\n", - "az.plot_posterior(samples, kind='hist');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hands-on: Bayesian Hypothesis testing II -- beak lengths difference between species" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Task**: Determine whether the mean beak length of the Galapogas finches differs between species. For the mean of each species, use the same model as in previous hand-on section:\n", - "\n", - "- Gaussian likelihood;\n", - "- Normal prior for the means;\n", - "- Uniform prior for the variances.\n", - "\n", - "Also calculate the difference between the means and, for bonus points, the _effect size_, which is the difference between the means divided by the pooled standard deviations = $\\sqrt{(\\sigma_1^2+\\sigma_2^2)/2}$. Hugo will talk through the importance of the _effect size_.\n", - "\n", - "Don't forget to sample from the posteriors and plot them!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with pm.Model() as model:\n", - " # Priors for means and variances\n", - " μ_1 = pm.Normal('μ_1', mu=10, sd=5)\n", - " σ_1 = pm.Uniform('σ_1', 0, 10)\n", - " μ_2 = pm.Normal('μ_2', mu=10, sd=5)\n", - " σ_2 = pm.Uniform('σ_2', 0, 10)\n", - " # Gaussian Likelihoods\n", - " y_1 = pm.Normal('y_1', mu=μ_1, sd=σ_1, observed=df_fortis['blength'])\n", - " y_2 = pm.Normal('y_2', mu=μ_2, sd=σ_2, observed=df_scandens['blength'])\n", - " # Calculate the effect size and its uncertainty.\n", - " diff_means = pm.Deterministic('diff_means', μ_1 - μ_2)\n", - " pooled_sd = pm.Deterministic('pooled_sd', \n", - " np.sqrt(np.power(σ_1, 2) + \n", - " np.power(σ_2, 2)) / 2)\n", - " effect_size = pm.Deterministic('effect_size', \n", - " diff_means / pooled_sd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# bust it out & sample\n", - "with model:\n", - " samples = pm.sample(2000, njobs=1)\n", - "az.plot_posterior(samples, var_names=['μ_1', 'μ_2', 'diff_means', 'effect_size'], kind='hist');" - ] } ], "metadata": { @@ -4854,5 +4696,5 @@ "toc-autonumbering": true }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/notebooks/04-instructor-finches.ipynb b/notebooks/04-instructor-finches.ipynb index 3b02912..ce318cb 100644 --- a/notebooks/04-instructor-finches.ipynb +++ b/notebooks/04-instructor-finches.ipynb @@ -4,7 +4,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING (theano.configdefaults): install mkl with `conda install mkl-service`: No module named 'mkl'\n" + ] + } + ], "source": [ "import pandas as pd\n", "import janitor as jn\n", @@ -101,44 +109,44 @@ " \n", " \n", " \n", - " 189\n", - " 19854\n", + " 170\n", + " 19592\n", " scandens\n", - " 14.0\n", - " 9.6\n", + " 13.7\n", + " 9.5\n", " 1\n", " \n", " \n", - " 240\n", - " 21290\n", + " 182\n", + " 19740\n", " scandens\n", - " 13.9\n", - " 8.7\n", + " 13.0\n", + " 8.9\n", " 1\n", " \n", " \n", - " 142\n", - " 19374\n", - " scandens\n", - " 13.9\n", - " 9.5\n", - " 1\n", + " 29\n", + " 19382\n", + " fortis\n", + " 9.8\n", + " 7.7\n", + " 0\n", " \n", " \n", - " 192\n", - " 19882\n", + " 176\n", + " 19622\n", " scandens\n", - " 13.7\n", - " 9.5\n", + " 13.2\n", + " 9.3\n", " 1\n", " \n", " \n", - " 104\n", - " 21266\n", - " fortis\n", - " 12.2\n", - " 11.1\n", - " 0\n", + " 188\n", + " 19852\n", + " scandens\n", + " 13.1\n", + " 8.5\n", + " 1\n", " \n", " \n", "\n", @@ -146,11 +154,11 @@ ], "text/plain": [ " band species beak_length beak_depth species_enc\n", - "189 19854 scandens 14.0 9.6 1\n", - "240 21290 scandens 13.9 8.7 1\n", - "142 19374 scandens 13.9 9.5 1\n", - "192 19882 scandens 13.7 9.5 1\n", - "104 21266 fortis 12.2 11.1 0" + "170 19592 scandens 13.7 9.5 1\n", + "182 19740 scandens 13.0 8.9 1\n", + "29 19382 fortis 9.8 7.7 0\n", + "176 19622 scandens 13.2 9.3 1\n", + "188 19852 scandens 13.1 8.5 1" ] }, "execution_count": 2, @@ -184,6 +192,26 @@ "unknown_df = df.query(\"species == 'unknown'\")" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((121, 5), (127, 5), (1, 5))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fortis_df.shape, scandens_df.shape, unknown_df.shape" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -260,7 +288,7 @@ ], "source": [ "with beak_depth_model:\n", - " trace = pm.sample(2000, tuning=1000)" + " trace = pm.sample(2000, tune=1000)" ] }, { @@ -704,5 +732,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }