clusterK (20)

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "FDW0_THqg8LC" }, "source": [ "(dependent_density_regression)=\n", "# Dependent density regression\n", ":::{post} 2017\n", ":tags: mixture model, nonparametric\n", ":category: intermediate\n", ":author: Austin Rochford\n", ":::\n", "\n", "In another [example](dp_mix.ipynb), we showed how to use Dirichlet processes to perform Bayesian nonparametric density estimation. This example expands on the previous one, illustrating dependent density regression.\n", "\n", "Just as Dirichlet process mixtures can be thought of as infinite mixture models that select the number of active components as part of inference, dependent density regression can be thought of as infinite [mixtures of experts](https://en.wikipedia.org/wiki/Committee_machine) that select the active experts as part of inference. Their flexibility and modularity make them powerful tools for performing nonparametric Bayesian Data analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wSEx-eTag8LD", "outputId": "a962b5ff-d107-47f8-b413-5dc0480648bf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on PyMC v5.16.2\n" ] } ], "source": [ "from io import StringIO\n", "\n", "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", "import pytensor.tensor as pt\n", "import requests\n", "import seaborn as sns\n", "\n", "from matplotlib import animation as ani\n", "from matplotlib import pyplot as plt\n", "\n", "print(f\"Running on PyMC v{pm.__version__}\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "0iVlIVjig8LE" }, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina'\n", "plt.rc(\"animation\", writer=\"ffmpeg\")\n", "blue, *_ = sns.color_palette()\n", "az.style.use(\"arviz-darkgrid\")\n", "SEED = 1972917 # from random.org; for reproducibility\n", "np.random.seed(SEED)" ] }, { "cell_type": "markdown", "metadata": { "id": "3VHUk32Mg8LE" }, "source": [ "We will use the LIDAR data set from Larry Wasserman's excellent book, [_All of Nonparametric Statistics_](http://www.stat.cmu.edu/~larry/all-of-nonpar/). We standardize the data set to improve the rate of convergence of our samples." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cVuo7yrRg8LE", "outputId": "bc357830-c080-453c-ff24-8154c328817b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/home/fonnesbeck/repos/pymc-examples/.pixi/envs/default/lib/python3.12/site-packages/urllib3/connectionpool.py:1099: InsecureRequestWarning: Unverified HTTPS request is being made to host 'www.stat.cmu.edu'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#tls-warnings\n", " warnings.warn(\n" ] } ], "source": [ "DATA_URI = \"http://www.stat.cmu.edu/~larry/all-of-nonpar/=data/lidar.dat\"\n", "\n", "\n", "def standardize(x):\n", " return (x - x.mean()) / x.std()\n", "\n", "\n", "response = requests.get(DATA_URI, verify=False)\n", "df = pd.read_csv(StringIO(response.text), sep=r\"\\s{1,3}\", engine=\"python\").assign(\n", " std_range=lambda df: standardize(df.range), std_logratio=lambda df: standardize(df.logratio)\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "i30x-q2Cg8LE", "outputId": "791768de-d65e-47f8-9aa2-ffecea186946" }, "outputs": [ { "data": { "text/html": [ "

	range	logratio	std_range	std_logratio
0	390	-0.050356	-1.717725	0.852467
1	391	-0.060097	-1.707299	0.817981
2	393	-0.041901	-1.686447	0.882398
3	394	-0.050985	-1.676020	0.850240
4	396	-0.059913	-1.655168	0.818631

\n", "

" ], "text/plain": [ " range logratio std_range std_logratio\n", "0 390 -0.050356 -1.717725 0.852467\n", "1 391 -0.060097 -1.707299 0.817981\n", "2 393 -0.041901 -1.686447 0.882398\n", "3 394 -0.050985 -1.676020 0.850240\n", "4 396 -0.059913 -1.655168 0.818631" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "tylbzDhcg8LE" }, "source": [ "We plot the LIDAR data below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628 }, "id": "HuFM6Wq8g8LE", "outputId": "4240b043-428a-4923-9a48-3e1f24461842" }, "outputs": [ { "data": { "image/png": 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EM1aulO3zPJaCaPs8E63BHHmWVhQ5Ew8UA95bAJD5jH6q3nzzzSbDxcyyLHV2dqbl2AAAAACApcHUVeTxWujEqGVZ2tNcox37ehZVUeMqyNPu5urZxImn0MEJ2DgkY54H//5mRdvndfYlnqhoqilL2evDewvJEJwKaygQ0mQoIpczXxWXJNABxM/o7nn++edlWVbKB2Vx9QwAAAAAIJlMXkUer2udGK3zedS2rTbuWTCugjy1batlOH0Csnmex1LS0uAzklxp2WymDV8seG/BFNu2deTUuPZ3n9XjA+fmJO3yLalpXblaGnxqrCzmHCsQp6SkJmPZiNEEzGI3rW3baUnkAAAAAACWHpNXkS/GtU6Mbl1Tqr2t9drVMRBTi7Bqr1u7m6tJrCQom+d5LCWm2udtWV1scFUL470FE3r9wQW/FyK21Nk3qs6+UVV5XdrTXMP3AhAHo8mVVatWxXX/F198URcuXJCkOUmSwsJCFRUVSZICgYCmpqZmfxZNxrjdbpWWlia4YgAAAAAArs30VeTxiuXEaJ3Po8e2b1LX6XHt7/brUP/onISQI89SU02ZWjb7tGU1VyibkK3zPJYa0+3zUoH3FhJ1+ORYXBWNgyOT2rGvR23barV1TWlyFwfkCKPJlYMHD8Z834ceekif/exnJV38krv55pv11re+VRs3bpTPN7fM0u/36xe/+IX++Z//WQcPHtTMzIzC4bDuvPNO7dixw+RTAAAAAADgCqavIo9HPCdGLctSY2WJGitLFhxODzOydZ7HUpRt7fN4byERvf5g3O91SZqcntHOA8e1t7WeChYgBnnpOGhbW5s+85nPKBwO64YbbtD+/fv1pS99SbfddtsViRVJ8vl8uu222/SlL31Jjz76qF71qlcpFArp7/7u7/SZz3wmDc8AAAAAALCURK8iT4fFnhj1FDq01utW/aoirfW6ObmaJC0NZuZwpHKex1IVbZ9X5XXFdP9qr1t7W+vTdhU/7y0shm3b2tUxsKgqLeliguW+jhOMYgBikPLkyk9/+lN97Wtfk23bWr16tfbt26cbb7wx5sffdNNN+uY3v6nKykrZtq2HHnpI//Zv/5bEFQMAAAAAlrroVeTpwInRzBad55GIVM/zWMqi7fMevGODbllffkXS1JFn6db15Xrwjg1q374xrVfv897CYhw5NZ7QfCFJOjEyoa7T44ZWBOSulF+28rWvfU3SxVLlv/iLv1BZWVncMcrLy3Xffffp3e9+92zMN7zhDUbXCQAAAADApVoafEZa9MSDE6OZLxvneSx12dI+j/cWFqP9qN9MnG6/GitLjMQCclVKvy1+/etf68iRI7IsSytXrtTrXve6Rcd6/etfr1WrVunMmTPq7u7Wr3/9a73yla80uNrY8SWFVLn0vcb7DrgSewRYGHsEWBh7BNfy6htKVOV1a3BkIiXHcxXkac/v1igvL7amE8GpsIYuOUlcYfgkMXtkfr+xskhtb6/Tzm/3xj/P4+11+o2VRUlcHRZSdF2Biq4rMBIrGXuE9xbiEZwK61C/mYsADvaP6qVQhO8RYAEpTa709vbKtm1ZlqV169YlHG/9+vU6c+aMJOmZZ55JW3KltLQ0LcfF0lZSwtUDwELYI8DC2CPAwtgjmM/n79islgee0EQoktTjuJ35euCuLXptzfUL3s+2bT3xq1E98sRz+vEzQ4rMvNwjPz/P0m0bKvSOrTfotWvLjZ7IYo9c6S0NpVq9Yrl2th9T/1DwmvdfX1Gkv2/ZqPpX8G+Zi0zuEd5biNULQwFFDI1KidjSBatQryxNToKO7xHkgpQmV4aGhmZvL1u2LOF4brd79vbw8HDC8QAAAAAAWEj9K0r0wF1b9N5HuuJKsFznyFO5p1DPj127D36sJ0Z7nj+/4MnWyIytf/mlX//yS7/WVXjU1rKJk61JVv+KEv3oI7+tw786p0cOP6sfPT034eXIs3TbBp/esfUGbV1bxpXbiBnvLcTiJcOJ/+BUci8kALJdSpMrFy5cmL19aaJlsS6NcWnsVBsbG0vbsbG0WJY1m9k/f/68bNvQ5QhAjmCPAAtjjwALY48gVjdeX6C9rfX65PcHYmoRVn29W3uaa1RbsUxHTo1rf/dZHeofnXN1sSPPUtO6MrU0rFRjZbEsy17w/2s+cXIsrjZB/UNB3X7/f6jt7XV67ZrSmB5zOfZI7OrK8/Wp5ir92S03aDgQ0kuhiJZdNs/j/PnzaV4lTEvFHuG9hYVEpsy2rZyZmpDJ0558jyDdTHegSmly5frrL5Yz27atX/ziFwoEAioqWlxp2fj4uI4dOzabifd6vcbWGS8+CJAOtm3z3gMWwB4BFsYeARbGHsG11FYs02PbN6rr9Lj2d/uvniypKVPLZp+2rC6e/f+ujZXFaqwsvuYQ7YXef73+YNzzFyRpcnpGO7/dq72t9arzeeJ7wpdhj8RmmTNfa8pdc/6Of7elIdl7hPcWrmaFp0D5loy0BnPkWbreU5C09xXfI8gFKU2u1NbWSrqYpQyHw/rKV76i/+//+/8WFeurX/2qwuHwbLy6ujpj6wQAAAAA4Fosy1JjZYkaK0uumSy5nKfQsaghwbZta1fHQNyJlajJ6Rnd13FC7ds30jYIAHKMp9ChpnXl6uxLfKh9U02Z0WH2QC7KS+XB1q9fr1e96lWSLv5C+I1vfEPt7e1xx2lvb9c3vvGN2V8EX/WqV2n9+vUmlwoAAAAAQMw8hQ6t9bpVv6pIa73upJ2QOnJqXIMj157bspATIxPqOj1uaEUAgEzS0uAzE2ezmThALktpckWS7rnnHtm2LcuyNDMzo7/4i7/QRz7yEQ0ODl7zsYODg/rwhz+sv/iLv5gtHbMsS/fcc08KVg4AAAAAQHq1H/WbidNtJg4AILM0Vharyuu69h0XUO11a8vqYkMrAnJXymu7fvd3f1c//OEP1dnZKcuyZNu2fvSjH+lHP/qR1q9fr5tuukmvetWr5PF4ZFmWAoGAnn32Wf3iF79Qf3+/JM0mVSTplltuUXNzc6qfBgAAAAAAKRWcCutQf+KtXiTpYP+oglNhWr4AQI6xLEt7mmu0Y1/PolpIugrytLu5mtaRQAzS8lvUZz/7Wd1zzz36t3/7t9mNatu2jh8/rr6+vqs+JjrgyLKs2aTMG9/4RrW1taVs3QAAAAAApMtQIGRkSLF0cdjxcCBEcgUAclCdz6O2bbXaeeB4XAkWV0Ge2rbVqs7nSeLqgNyR8rZgklRQUKD7779f9957r5xO52wlyqWJlkv/k+YmVZxOpz72sY/pK1/5igoKCtLxFAAAAAAASKnJUMRovAnD8QAAmWPrmlLtba2PuUVYtdetva312rqmNLkLA3JI2i5RsSxL7373u/XWt75Vjz76qL773e/qzJkzV71vNMGyatUqve1tb9P//J//UxUVFalcLgAAAAAAaeVy5huN5zYcDwCQWep8Hj22fZO6To9rf7dfh/pH51RAOvIsNdWUqWWzT1tWF9MKDIhT2ut/Kyoq9OEPf1gf/vCHNTQ0pF/+8pcaHR3V+fPnJUklJSUqLy/XjTfeSEIFAAAAALBkVRQ5lW/JSGswR56lFUXOxAMBADKaZVlqrCxRY2WJglNhDQdCmghF5Hbma0WRk/aQQAIyavdUVFSQQAEAAAAA4Co8hQ41rStXZ1/iQ+2baso4oQYAS4yn0MFnP2BQWmauAAAAAACA+LU0+MzE2WwmDgAAwFJFcgUAAAAAgCzRWFkc83Di+VR73dqyutjQigAAAJYmkisAAAAAAGQJy7K0p7lGroLF/d95V0GedjdXM7QYWGKCU2ENjkyo50xAgyMTCk6F070kAMh6GdFkr7e3Vz/5yU/U1dWlU6dO6fz583rppZdkWZaeeeaZK+4/Pj6uYDAoSXI6nfJ6valeMgAAAAAAaVHn86htW612HjiuyemZmB/nKshT27Za1fk8SVwdkNmCU2ENBUKaDEXkcuarIocHetu2rSOnxrW/+6weHziniP3yz/ItqWlduVoafGqsLCbhCgCLkNZvj76+Pn3605/Wz3/+89m/s217gUdc9POf/1wf+tCHJEkul0v//u//LpcrsbJoAAAAAACyxdY1pdrbWq9dHQMaHJm85v2rvW7tbq4msYIlaSkmGXr9wQU/HyK21Nk3qs6+UVV5XdrTXMPnAwDEKW3JlW9/+9vavXu3pqamZNv2nC8vy7IWTLLcfPPNWrlypc6cOaPJyUn96Ec/0tve9rYUrBoAAAAAgMxQ5/Pose2b1HV6XPu7/TrUPzrnpLEjz1JTTZlaNvu0ZXXyThovpUoAZJ+lmGQ4fHIsrsq2wZFJ7djXo7Zttdq6pjS5iwOAHJKW33Z+9KMf6c///M/nJFVs29aqVatUUlKi3t7eBR+fl5en3/md39HXv/51SdLBgwdJrgAAAAAAlhzLstRYWaLGyhIFp8IaDoQ0EYrI7czXiiQmOWzb1pPPnV9SlQDIPksxydDrD8bdMlCSJqdntPPAce1trc/65BIApErKkyvDw8P6+Mc/Lkmzv2C1trbqj//4j7V69Wr9+te/1i233HLNODfffLO+/vWvX/yF7sknk7pmAAAAAAAynafQkZKKkZ7nz+vD3zqmwZGJq/788kqAT9y6VqXuAipbkFJLMclg27Z2dQzE/ZyjJqdndF/HCbVv30hSFABikPLfZr785S9rcvJiKWZ+fr7a2tp02223zf481g/vG2+8UQ6HQ+FwWGNjYzp9+rRWr16dlDUDAAAAAADppwMv6L2PdGkiFInp/oMjk3r3t56e83dUtiDZlmqS4cip8ZhmMC3kxMiEuk6Pq7GyxNCqACB35aXyYJFIRB0dHbIsS5Zl6d3vfvecxEo8HA6H1q5dO/vnX/3qV6aWCQAAAAAALtPrD8aVWJlPtLLl7kef1u0PHVOvP2hohcBFJpMM2aT9qN9MnG4zcQAg16U0uXLs2DEFg0HZti2Hw6H3vOc9CcXz+Xyzt/1+PvgBAAAAAEgG27b1ye8PJJxYuVx0xsXhk2NG42JpW4pJhuBUWIf6R43EOtg/quBU2EgsAMhlKU2unDp1StLF1l833nijPJ7Eelde+vhgkCtdAAAAAABIhouVAFefsZKo6IwLKlhgwlJNMgwFQorYZmJFbGk4EDITDAByWEqTK+fOnZu9vXLlyoTjXdr3MhIxe/UMAAAAAAC4yFQlwHyiMy5s29DZYSxZSzXJMGm4qsx0lRoA5KKUJldMJ0POnz8/e7uoqCjheAAAAAAAYC6TlQALycYZF8g8SzXJ4HLmG43nNhwPAHJRSpMrZWVls7eHh4cTjjcwMDB7u7S0NOF4AAAAAABgLpOVANeSTTMukJmWapKhosipfOva94uFI8/SiiKnmWAAkMNSmlyJtgKzbVu9vb2anp5edKyTJ09qaGho9s/r169PeH0AAAAAAGAu05UAC8mmGRfITEs1yeApdKhpXbmRWE01ZfIUOozEAoBcltLkSkNDg6677jpZlqULFy6oo6Nj0bEeeeSR2dvl5eVau3atiSUCAAAAAIBLmK4EWEg2zbhAZlrKSYaWBp+ZOJvNxAGAXJfS5IrT6dTWrVtl27Zs29ZnP/tZjY/H30+1q6tL+/fvl2VZsixLb3rTm5KwWgAAAAAAYLISIBbZMuMCmWupJhkaK4tV5XUlFKPa69aW1cWGVgQAuS2lyRVJ+pM/+RNJF4fbDw0Nafv27RodjX0w3uHDh/X+979fMzMzsm1b+fn52r59e7KWCwAAAADAkmayEiAW2TLjAplrqSYZLMvSnuYauQoWd7rPVZCn3c3VsqwUZlMBIIulPLmyceNGNTc3y7ZtWZalnp4eveUtb9GXv/xl/epXv9LMzMwVj4lEInriiSf00Y9+VH/8x3+s8+fPzz7+rrvu0itf+cpUPw0AAAAAAJYMU5UA15JNMy6QuZZykqHO51Hbttq4n7urIE9t22pV5/MkaWVAYoJTYQ2OTKjnTECDIxPM50JGsGzbtlN90AsXLqi1tVXPPPOMLMuaTZRIUkFBgUKhi/1VLcvSq171Kv36179WOHxxw0Tva9u2Nm/erEceeUT5+em9quXFF19M6/GxdFiWpdLSUknS2NiY0rB9gYzGHgEWxh4BFsYeAeZn27Zuf+gXGhyZSOpxbl1frr992/qkHgNLx+GTY9p54Lgmp6+8kHc+0STD1jWlcR8vk75Hev1B7eoY0ODI5DXvW+11a3dzNYkVJF28e8S2bR05Na793Wf1+MA5RS65e74lNa0rV0uDT42VxVmZDEXqLV++3Gi8tCRXJOncuXPauXOnDh8+PPvmjy4lmjy53KX3e93rXqfPf/7z8njS/8FPcgWpkkm/qAGZiD0CLIw9AiyMPQIs7PjQS9qxryepM1EevGODGitLkhYfS08qkwyZ9j1i27a6To9rf7dfh/pH55yYduRZaqopU8tmn7as5sQ0UiOePRLP3q3yurSnuYYEIa4pZ5Ir0sUP+b179+qhhx7SuXPnLi5ong/z6DKLi4u1Y8cOvfvd7057xUoUyRWkSqb9ogZkGvYIsDD2CLAw9giwMMuy9MsXpvXeR7qSkmCp9rrVvn0jJ3lhXKqSDJn8PRKcCms4ENJEKCK3M18ripzyFDrSvSwsMbHukVRXnWHpyKnkStTU1JS+//3v6z/+4z/U1dWl4eHhObNXSkpK1NDQoNe//vX6/d//fRUVFaVxtVciuYJUyeRf1IBMwB4BFsYeARbGHgEWFt0jPc+f14e/1W20RZirIE97W+u56hhJl8wkA98jwMJi2SO9/qB27OuJK7ESxXcJriUnkyuXs21b58+f1/T0tEpLS1VQUJDuJS2I5ApShV/UgIWxR4CFsUeAhbFHgIVdukdefPFFHTl1/qqVAPHiamPkCr5HgIVda49cnO91LKZWYPOhChILMZ1cycj6v0s3GgAAAAAAyCyWZamxskSNlSVXVAK8ODmtT//4VwzSBgDE5cip8YQSK5J0YmRCXafHmd+FlMjI5AoAAAAAAMgOnkLHFW2VHtu+iUHaAIC4tB/1m4nT7Se5gpQguQIAAAAAAIxaqLKFQdq4muBUWEOBkCZDEbmc+argfQIsKcGpsA71jxqJdbB/VMGpMJ8hSLqUv8Nuvvnm2dsVFRX63Oc+pxUrVsQdZ2hoSK2trZIu/tLW2dlpbI0AAAAAAMCMq1W2ANLF+QpHTo1rf/dZPT5wbk6FU74lNa0rV0uDT42VVDgBuW4oEEpoftelIrY0HAjx3YOkS/k77Pnnn5dlWbJtW88//7zuuOMOPfTQQ7rhhhviihMOh/X8889LEl+wAAAAAAAAWaTXH9SujoF55ytEbKmzb1SdfaOq8rq0p7mG2TxADpsMRYzGmzAcD7iavHQd2LIsWZY1m2B55pln0rUUAAAAAAAApMjhk2Pasa8n5sHVgyOT2rGvR4dPjiV3YQDSxuXMNxrPbTgecDVpS65IF8s/LcvSuXPndNddd+nw4cPpXA4AAAAAAEBWC06FNTgyoZ4zAQ2OTCg4FU73kubo9Qe188BxTU7PxPW4yekZ7TxwXL3+YJJWBiCdKoqcyjfUnMiRZ2lFkdNMMGABaW089+pXv1pPPvmkLMvSSy+9pLvvvlt/93d/pze96U3pXBYAAAAAAEDWyJbZJbZta1fHQNyJlajJ6Rnd13FC7ds30iIeyDGeQoea1pWrsy/xofZNNWXMW0FKpKVyxbYvfst/6lOf0h/+4R/OVrCEQiF99KMf1f79+9OxLAAAAAAAgKzS6w/q9oeO6e5Hn9ZP+s9dMRA6Orvk7kef1u0PHTNS+bHY6pgjp8ZjbgU2nxMjE+o6PZ5QDACZqaXBZybOZjNxgGtJawovPz9ff/mXf6mysjJ95StfkWVZikQi+su//EudO3dOf/Inf5LO5QEAAAAAAGSswyfH4mqxFZ1d0ratVlvXlMZ1LBPVMe1H/XEdcz7t3X41VpYYiQUgczRWFqvK60ooCVvtdWvL6mKDqwLml9aZK1Ef+tCHtGvXrtkh97Zt6wtf+IL++q//Ot1LAwAAAAAAyDipnF1iojomOBXWof7E2/1I0sH+0YybJQMgcZZlaU9zjVwFiztl7SrI0+7matoGImUyIrkiSXfeeaf+7u/+Tg6HYzbB8s1vflP33nuvIpFIupcHAAAAAACQEUzNLom2bV/I4ZNj2rGvJ+YryaPVMYdPjs35+6FA6IqkzGJFbGk4EDITDEBGqfN51LatNu4Ei6sgT23balXn8yRpZcCVMia5Ikm/8zu/o/vvv18ul2s2wdLR0aH3ve99unDhQrqXBwAAAAAAsGiLnVVyuVTNLjFZHTMZMnvh7ITheAAyx9Y1pdrbWq8qryum+1d73drbWh93u0MgUWmduXI1r3vd6/Twww/r7rvv1vnz52Xbtn72s5/pXe96lx544AGVlNBTEwAAAAAAZAcTs0oul4rZJaaqY9q3b5RlWXI58xNZ6hXchuMByCx1Po8e275JXafHtb/br0P9o3M+Px15lppqytSy2actq2P//ARMyrjkiiTddNNN+uY3v6kdO3ZoaGhIknTs2DHdeeed2rt3ryoqKtK8QgAAAAAAgIX1+oPa1TEwb5VJdFZJZ9+oqrwu7WmuuWZLm2TMLvEUXnl6yGR1TGNliSqKnMq3ZKQ1mCPP0ooiZ+KBAGQ0y7LUWFmixsoSBafCGg6ENBGKyO3M14oi51U/u4BUyqi2YJeqqqrSo48+qjVr1si2bVmWpRMnTuiOO+7QyZMn0708AAAAAACAeZmaVXK5VM0uMVkdI0meQoea1pUbidlUU8ZJVWCJ8RQ6tNbrVv2qIq31uvkMQEbI2OSKJPl8Pu3bt0833XTTbILlzJkzuvPOO/XUU0+le3kAAAAAAABXMDmr5Ir7pGB2STKqYySppcFnJGbLZjNxAABIREYnVySptLRU3/jGN/S6171uNsFy7tw5fexjH6OXHgAAAAAAyCimZpXY9tXLU1IxuyRZ1TGNlcUxD6ieT7XXrS2ri00sDQCAhGR8ckWSXC6XHnjgATU3N88mWMLhcLqXBQAAAAAAMIfJWSVXE51dYsJ8s0uSVR1jWZb2NNfIVbC401Gugjztbq7mYlsAQEZIS3JlMV+CDodDf//3f693vOMdswkWAAAAAACATGJ6VsnlUjG7JJnVMXU+j9q21cadYHEV5KltW63qfB6jawMAYLFSnlxZtWqVVq5cqZUrVyo/P/4v609+8pP64Ac/KNu25y2RBQAAAAAASLVkzSq5XLJnlyS7OmbrmlLtba2PuUVYtdetva312rqm1MyiAAAw4MrLE5Ls4MGDCcf4wAc+oE2bNml4eNjAigAAAAAAABKXjFklV6ssic4uSaT92EKzS6LVMZ19iSeK5quOqfN59Nj2Teo6Pa793X4d6h+d82/nyLPUVFOmls0+bVldTAcTAEDGSXlyxZTXve516V4CAAAAAADArGTNKrlcdHbJjn09mpyeiTtuLLNLWhp8RpIr81XHSBefR2NliRorSxScCms4ENJEKCK3M18ripxXTcoAAJApsmKgPQAAAAAAQKZL5qySyyV7dkm0OiYRC1XHXM5T6NBar1v1q4q01usmsQIAyHgkVwAAAAAAAAxI9qySyyVzdkm0Oibe5E1ULNUxAABkM5IrAAAAAAAABkRnlZgw36ySy0Vnlzx4xwbdsr78iuSOI8/SrevL9eAdG9S+feM1K1Yuj53M6hgAALIZNZYAAAAAAACGpGJWyeWSObskWh2zq2NAgyOT17x/tdet3c3VJFYAxCw4FdZQIKTJUEQuZ74qmLmELGH0XVpXVzfnz5Zl6ZlnnlnwPiZc7TgAAAAAAACpFp1VEksiYj7xzCq5nKfQYfykZLQ6puv0uPZ3+3Wof1QR++WfO/IsNdWUqWWzT1tWF9MKDMA12batJ587r/3dZ/X4wLk5nyn5ltS0rlwtDT41VvKZgsxl9NvWtm0j9wEAAAAAAMhG0VklO/b1aHJ6Ju7HZ+qskmRWxwBYWnqeP68Pf+uYBkcmrvrziC119o2qs29UVV6X9jTXUA2HjGR85oplWdf8BcDULwiZ9osGAAAAAABArs8q8RQ6tNbrVv2qIq31ukmsAIjZTwdeUMsDT8ybWLnc4Mikduzr0eGTY8ldGLAIRr/9Xv3qVxu5DwAAAAAAQDZjVgkAzNXrD+q9+3o0EYrE9bjJ6RntPHBce1vr+YxERrFs+nQl7MUXX0z3ErBEWJal0tJSSdLY2Bht9oDLsEeAhbFHgIWxR4CFsUcWx7ZtZpUsEewRYH62bev2h34Rc8XK1VR73WrfvpHPSiza8uXLjcajbhMAAAAAACBJmFUCANKRU+MJJVYk6cTIhLpOj6uxssTQqoDE8A0OAAAAAACQAp5Ch/FkSnAqrKFASJOhiFzOfFWQsAGQgdqP+s3E6faTXEHG4NsWAAAAAAAgi9i2rSOnxrW/+6weHzg3p9VYviU1rStXS4NPjZW0GgOQfsGpsA71jxqJdbB/VMGpMElkZATehQAAAAAAAFmi1x/Uro4BDY5MXvXnEVvq7BtVZ9+oqrwu7WmuYQA0gLQaCoTmJIETEbGl4UCI5AoyQl66FwAAAAAAAIBrO3xyTDv29cybWLnc4Mikduzr0eGTY8ldGAAsYDIUMRpvwnA8YLFIrgAAAAAAAGS4Xn9QOw8c1+T0TFyPm5ye0c4Dx9XrDyZpZQCwMJcz32g8t+F4wGIZrZ/6xCc+YTJczCzL0qc+9am0HBsAAAAAACCZbNvWro6BuBMrUZPTM7qv44Tat29kBguAlKsocirfkpHWYI48SyuKnIkHAgwwmlw5cOBAyr+kbdsmuQIAAAAAAHLWkVPjMbcCm8+JkQl1nR5XY2VJ3I8NToU1FAhpMhSRy5mviiIn8w4AxMxT6FDTunJ19iU+1L6ppozPH2SMtL4TbXtuuvJaiZl47w8AAAAAAJDt2o/6zcTp9secXLFtW0dOjWt/91k9PnBuzhXn+ZbUtK5cLQ0+NVYWc34GwDW1NPiMJFdaNvsMrAYww3hy5fIEyLVc+gV8rcdeft94jwUAAAAAAJBNglNhHepP/ISkJB3sH1VwKnzNq757/UHt6hiYt1omYkudfaPq7BtVldelPc01qvN5jKwRQG5qrCxWldetwZGJRceo9rq1ZXWxwVUBiTGaXPnJT34S832PHj2qPXv2aHx8XLZtq6ysTG95y1t00003ac2aNfJ4Ln4pB4NBnTx5Uk899ZR+8IMf6Ny5c7IsSyUlJfrkJz+pzZs3m3wKAAAAAAAAGWMoEDIyp0C6mBQZDoQWTK4cPjmmnQeOxzzfZXBkUjv29ahtW622rik1s1AAOceyLP3179Zox74eTYQicT/eVZCn3c3VVMoho1h2Gso/Ojs7tXPnTk1PT+u6667Thz70Id11111yOBbO9YTDYf3jP/6jvvjFL+rChQtyOBxqa2vTrbfemqKVX92LL76Y1uNj6bAsS6WlpZKksbExqreAy7BHgIWxR4CFsUeQTLkws4I9kh49ZwK665FfGov3yF03qn5V0VV/1usPase+npgTK5dyFeRpb2v9kq5gYY8AC7MsS798YVrvfaQrrgSLqyCPBC6MWL58udF4Kf9N7uTJk7r33nsVCoW0bNkyPfjggzFXnzgcDm3fvl2bNm3Se97zHr300ku699579e1vf1tVVVVJXjkAAAAAALFjZgVMcDnzjcZzzxPPtm3t6hhYVGJFkianZ3Rfxwm1b9/I+xnAvP5bzfVqf+9r9eFvdcfUIqza69bu5uolnbhF5spL9QGjVSeWZelP//RPF9XWa/Pmzdq5c6ckKRQK6Ytf/KLpZQIAAAAAsGi9/qBuf+iY7n70af2k/9wVbZ2iMyvufvRp3f7QMfX6g+lZKDJeRZFT+YZyFY48SyuKnFf92ZFT4/POWInViZEJdZ0eTygGgNxX/4oS/Z8dm/TgHRt0y/ryKz7jHHmWbl1frgfv2KD27RtJrCBjpbRyJRAIqLOzU5JUVFSk22+/fdGxWlpa9LnPfU6BQEAHDx5UIBBQUdHVy1oBAAAAAEgVZlYsLBdapKWSp9ChpnXl6uxLfKh9U03ZvP/W7Uf9CceXpPZuvxorS4zEApC7LMtSY2WJGitLFJwKazgQ0kQoIrczXyv4XkCWSOm7tLu7W6FQSJZl6cYbb1RBQcGiYxUUFOimm27Sv//7v2t6elpdXV164xvfaG6xAAAAAADEqdcfjCuxEjU5PaOdB47n7MwKWqQlpqXBZyS50rLZd9W/D06Fdag/8fiSdLB/VMGpMCdGAcTMU+jgMwNZKaXv2qGhodnbJobHRIeEXR4bAAAAAIBUY2bF1fX6g9rVMTBvy6loi7TOvlFVeV3a01yTkwmmRDRWFqvK60qobVe1160tq4uv+rOhQOiK1nWLFbGl4UCIE6UAgJyX0pkrY2NjV729WOfPn7/qbQAAAAAAUo2ZFVc6fHJMO/b1xPzvEm2RdvjkWHIXlmUsy9Ke5hq5ChZ3GsdVkKfdzdXzJu0mQ5FElneFCcPxAADIRClNrkSrVWzb1i9/+UuFw+FFx5qentZTTz11RWwAAAAAANLB5MyKTBW4MK2BoYB+eSagwZEJBafm///1ibZI6/UHE11uXIJTYQ2OTKgnhueWDnU+j9q21cadYHEV5KltW+2C1UAuZ36iy5vDbTgeAACZKKU1mq961askXbziYnx8XAcOHFj0UPsDBw5ofPzlq3misQEAAAAASLVcnlkRnZfSftSvQwPnFJl5uX/UfPNSsqVFWrbNgtm6plR7W+sXbLN2qWqvW7ubq6/ZZq2iyKl8S0ZagznyLK0ociYeCACADJfS39S2bNmi5cuXa2xsTLZt62//9m/1G7/xG9qwYUNccXp6evSZz3xGlmXJtm0tX75cW7ZsSdKqAQAAAABYWK7OrFjsvBSTLdIaK0sSijOfbJ0FU+fz6LHtm9R1elz7u/061D86573nyLPUVFOmls0+bVkdW1LIU+hQ07pydfYlniBsqinLiPcuAADJltJvu7y8PN1555360pe+JMuyFAgE9M53vlN/+qd/qjvuuOOaX/i2bWvfvn367Gc/q5deekm2bcuyLLW2tiovL6UdzgAAAAAAmJWLMysOnxyLq61XdF5K27Za/d9fDBlZQ3u3PynJlUSe29Y1pcbXEy/LstRYWaLGyhIFp8IaDoQ0EYrI7czXiiLnopIbLQ0+I8mVls2+hGMAAJANUn4pwd13362Ojg49++yzsixLL730kvbs2aOvfvWrestb3qKNGzfqhhtukMfjmU3APPfcczp27Jh++MMfamRkZDapIklr1qzRe9/73lQ/DQAAAAAAZuXazIpE5qV89Nu9Chkq40lGi7REZ8Hsba3PiAqWKE+hw8i/T2Nlsaq8roQqjqq9bm1ZXZzwWgAAyAYpT644nU499NBDuuuuu/TrX/96trXXCy+8oEceeUSPPPLIvI+17Yu/nEUf88pXvlIPPfSQCgoKUrV8AAAAAACukEszKxKdl3IhbKg/msy3SMuWWTDpYFmW9jTXaMe+nkX9+7gK8rS7uTrn/l0AAJhPWnpprVy5Ut/61rf0hje8YbYK5dKhd1f7T9Kc+7zhDW/Qt771Lfl8lJsCAAAAANIrOrPChHTPrDAxL8Ukky3STM6CyUV1Po/attXKVRDf6SJXQZ7attVmVEUPAADJlrZBJddff70eeOABfeELX9CWLVvmJFGuJvrzxsZGfeELX9ADDzyg66+/PoUrBgAAAABgfi0NZi7+S/fMivaj/rQe/3ImW6SZem7t3Zn1b2TS1jWl2ttaryqvK6b7V3vd2ttanxGzaAAASKX0XQrzX970pjfpTW96k55//nl1dXWpp6dHo6OjOn/+vCSppKRE5eXlqq+v15YtW/SKV7wizSsGAAAAAOBKuTCzIjgV1qH+xIeam2KyRZrJ55aMWTCZpM7n0WPbN6nr9Lj2d/t1qH90Tss7R56lppoytWz2acvqYlqBAQCWpIz5LeAVr3iFXvGKV+j3fu/30r0UAAAAAADilgszK4YCISNzY0wx2SLN5HMzPQsmE1mWpcbKEjVWlig4FdZwIKSJUERuZ75WFDlz+rkDABCLlH4THjlyRA8//PDsn//sz/5Mq1atSuUSAAAAAABImujMip0HjseVYMmUmRWTBuebmGCyRZrp52ZyFkym8xQ6SKYAAHCZlH4zPvXUU+rs7JRlWVq1ahWJFQAAAABAzonOrNjVMRBTi7Bqr1u7m6vTnliRJJfB+SaJMt0izfRzMzkLBgAAZJ+UJlcikZev6qiurk7loQEAAAAASJlsnVlRUeRUviUj7bPyLcnpyMuYFmkmn5vJWTAAACA7pTS54vV6Z28XFRWl8tAAAAAAAKRUNs6s8BQ61LSuXJ19iQ9+/+/ryvX2jRUZ0yLN5HMzOQsGQOYLToU1FAhpMhSRy5mvigz9DAeQWin9FKioqJi9/eKLL6by0AAAAAAApE02zaxoafAZSUC0bPapsbIko1qkmXxuAHKbbds6cmpc+7vP6vGBc3Oq3vItqWlduVoafGqszJzqQwCpldLf7LZs2SK3262JiQn19PTItm0+fAAAAAAAyCCNlcWq8rpiSobM59J5KZnUIs30c0Puo2Jhaer1BxdMCkdsqbNvVJ19o6ryurSnuSYj5mYBSK2UfhsUFhbq5ptv1j//8z9rfHxcP/rRj/TmN785lUsAAAAAAAALsCxLe5prtGNfj7F5KZnSIi0Zzw3pk6zEBxULS9vhk2NxtTMcHJnUjn09attWq61rSpO7OAAZxbJt28Aot9j5/X793u/9ngKBgHw+n/bv368VK1akcgnG0eIMqWJZlkpLSyVJY2NjSvH2BTIeewRYGHsEWBh7BJgr3hOM0svzUjL9BGMuP7dcl+zEx7UqFi51ecUC3yPZr9cfTCj5ure1ngqWBbBHkG7Lly83Gi/PaLQY+Hw+fepTn1JBQYHOnj2rd7zjHeru7k71MgAAAAAAwAK2rinV3tZ6VXldMd2/2uvW3tb6rEg+ZOpzC06FNTgyoZ4zAQ2OTCg4FU7q8bJNrz+o2x86prsffVo/6Z+bWJFebtV096NP6/aHjqnXH4wr/uGTY9qxryfmtnHRioXDJ8fiOg4yk23b2tUxsKjEiiRNTs/ovo4TJAyAJSTllStnzpyRJB07dky7du3SSy+9JMuytHnzZt1yyy2qq6tTeXm5li1bFlfcVatWJWO5MaFyBalChh9YGHsEWBh7BFgYewS4Otu2X56XMnBOkZmX90Yq56Ukw5znlqZZMLSgik2yq41MVCz8xsoivkey2JPPndfdjz6dcJwH79igxsoSAyvKPfyuhXQzXbmS8uRKbW3tFb8MJDrY3rIsPfPMM4kubdFIriBV+BICFsYeARbGHgEWxh4BFmZZlvKvW6ah8Qs6OzImd0FeSuelJFs6ZsEk0oJqKUl2qybbtnX7Q8dirli5mmqvW4/t2DR74o7vkezzse/0qbNvNOE4t64v19++bb2BFeUeftdCumV9W7CoSzdPNLFi2/ai/wMAAAAAAMlVdF2BqlcU6cZVRVrrdedMYkWSPIUOrfW6VZ+i50YLqtikolXTkVPjCSVWJOnEyISOnBpPKAbSJzgV1qH+xBMrknSwf5SWfsASkbbkinRlMgUAAAAAACDX9fqDcbe4ki4mCnYeOB73LJFsZirx0XV6/sRH+1F/QvFfjnPWSByJ+TupNhQIXTHDZ7EitjQcCJkJBiCjpfwSk23btqX6kAAAAAAAABnBVCVG+/aNS2IGi7HER7f/qnMwjFYs9I0qcGFaRdcVLOrxzN9Jn8lQxGi8CcPxAGSmlCdXPv3pT6f6kAAAAAAAABnBZCVGrg/NTkarpsvbvZmuWBgav7Co5Mq15u9EbKmzb1SdfaNLev5Osric+UbjuQ3HA5CZ0toWDAAAAAAAYCkxWYmR61LRqsl0xUJwKv54zN9Jv4oip/INFQM58iytKHKaCQYgo5FcAQAAAAAASAGGZscnFa2aTFcseArji8f8nczgKXSoaV25kVhNNWVXVEgByE0kVwAAAAAAAFKAodnxSUWrJtMVCxXF18V8f1Pzd2zb0JtqiWtp8JmJs9lMHACZj+QKAAAAAABACjA0Oz6paNVktGJhXVlc81ZMzt9B4hori1XldSUUo9rr1pbVxYZWBCDTkVwBAAAAAABIAYZmxydVrZqMVSw0rIzr/szfySyWZWlPc41cBYs7XeoqyNPu5mpZlqGMIICMR3IFAAAAAAAgBRiaHb9UtGoyVbHQWBl7xQLzdzJTnc+jtm21cSdYXAV5attWqzqfJ0krA5CJ0j5d6aWXXtKhQ4d09OhRDQ4Oanx8XIFAQDMzsfebtCxLnZ2dSVwlAAAAAABAYqKVGJ19iZ9UXypDs6OJj0TaZ12rVVO0YmHHvp5FzT9ZTMVCMubvLIX3QypsXVOqva312tUxENP7rtrr1u7mahIrwBKUtk/d6elpffGLX9S3vvUtBYPB2b9fzBAuyu0AAAAAAEA2aGnwGUmuLJWh2alKfEQrFnYeOB7XcRZbscD8ncxW5/Pose2b1HV6XPu7/TrUPzonGebIs9RUU6aWzT5tWV3MuUlgiUpLcuXcuXN6z3veo2eeeWY2mXLph1AsH0i2bcuyrEUlYwAAAAAAANIhFZUYuSZViY9UViwwfyfzWZalxsoSNVaWKDgV1nAgpIlQRG5nvlYUOakUApD65MrMzIx27typp59+WpJmEyQOh0MlJSUaGRmZTZysXLlSL730ksbHx69IwixbtkwlJSWpXj4AAAAAAMCipaMFVS5IVeIjVRUL0fk7JlqDLZX5O+nkKXSQTEHGCE6FNRQIaTIUkcuZrwqSfWmT8n/1jo4OHT58ePbLx+fz6ROf+ISampo0NDSkW265Zfa+Bw8elCRNTU3p6NGj+va3v61/+Zd/UTgcViQS0fvf/379j//xP1L9FAAAAAAAABYt1S2ockWqEh+pqFhg/g6AeNi2rSOnxrW/+6weHzg357Mv35Ka1pWrpcGnxkra1KVSyj95/+Ef/kHSxTeE1+vVt771Lfl8F/uEzvfCFxYWauvWrdq6davuuusufeQjH9Hzzz+vXbt2aWpqSnfeeWfK1g8AAAAAAJAohmYvTqpbNSWzYoH5OwBi0esPLvhdEbGlzr5RdfaNqsrr0p7mmiX/XZEqeak82Llz5/TMM8/IsixZlqWPfOQjs4mVWN144416+OGHVVZWJtu29elPf1q9vb1JWjEAAAAAAEByRCsxHrxjg25ZX678y645deRZunV9uR68Y4Pat2/kZNllPIUOrfW6Vb+qSGu97qyr3ojO30nEUpu/Ayw1h0+Oace+npjndA2OTGrHvh4dPjmW3IVBUoorV5566ilJF6tWXC6X3vrWty4qzurVq/XRj35Uu3btUiQS0QMPPKDPfe5zBlcKAAAAAACQfAzNXrqYvwNgIb3+YNztIyVpcnpGOw8c197WepLySZbSypXh4WFJF7881q9fr8LCwgXvPz09Pe/Pfv/3f19ut1u2bevxxx/XxMSE0bUCAAAAAACkUrZXYiB+0fk7roL4TtEt9fk7QK6zbVu7OgYWlXiVLiZY7us4Idu2r31nLFpKkyvnz5+fvV1RUXHFzwsKCub8eWpqat5YTqdTN9100+z9uru7Da0SAAAAAABgruBUWIMjE+o5E9DgyISCU+F0Lwk5Ijp/J9YWYdVet/a21mvrmtLkLgxA2hw5NR5zK7D5nBiZUNfpcUMrwtWk9BKISzNlV6taWbZs2Zw/j46OyuOZPwNfXl4+eztaFQMAAAAAAGCCbds6cmpc+7vP6vGBc4pccgFwviU1rStXS4NPjZXFtGbKAMGpsIYCIU2GInI581WRRW3VovN3uk6Pa3+3X4f6R+e83xx5lppqytSy2actq3m/Abmu/ajfTJxuvxorS4zEwpVS+g1zaaLkpZdeuuLnbrdbDodD4fDFqz+ef/553XDDDfPGu7Rt2OjoqMGVAgAAAACApazXH9SujoF5rxyO2FJn36g6+0ZV5XVpT3MNLZrSIJcSYMzfASBdTBQf6jdzrvtg/6iCU2E+P5Ikpf+qr3jFK2ZvXy0ZYlmWbrjhBg0ODkqSnnrqKf3Wb/3WvPEGBgZmb1/eUgwAAAAAAGSndFcgHD45FtcQ4cGRSe3Y16O2bbW0akqhXE6AeQodnAwFlqihQGhOojgREVsaDoT4PEmSlP6rrl27VtLFqwqiCZTL1dbWzv6so6ND73vf+656v1/84hc6efLk7J9XrFhheLUAAAAAACBVMqUCodcfjCuxEjU5PaOdB45rb2t91pzAz2YkwADkqslQxGi8CcPx8LKUDrRfvXr17JyUYDB41QTLzTffPHv7xIkTeuCBB664z+joqD7xiU/M+WVq8+bNSVgxAAAAAABItl5/ULc/dEx3P/q0ftJ/7oordqMVCHc/+rRuf+iYev3BpKzDtm3t6hiIO7ESNTk9o/s6TsyZOQvzEk2AJev9g4tVZ4MjE+o5E9DgyISCU+F0LwnIOi5nvtF4bsPx8LKU1wO95jWv0Q9+8ANJ0k9/+lNVVVXN+XlTU5PKysr04osvyrZtfe5zn9PPfvYzNTU1qaioSL/61a904MABnT9/XrZty7IsveY1r5HP50v1UwEAAAAAAAnKpAqEI6fG520xFasTIxPqOj3OAOEkMZUAa9++MeNnsGSLTKk6A3JFRZFT+ZaMtAZz5FlaUeRMPBCuKqWVK5J06623Srr4wfvd7373ip+7XC599KMfnU2c2LatI0eO6DOf+Yzuu+8+PfzwwxobG5u9v8Ph0J/+6Z+mavkAAAAAAMCQTKtAaD/qNxOn20wcXMlkAgyJy5SqMyCXeAodalpXbiRWU00Z81aSKOXJlaamJjU1NemNb3yjKioqdObMmSvuc/vtt+ud73znbIIlKlpWG026OBwO7d69WzfddFPK1g8AAAAAABKXaS24glNhHeofNRLrYP8o7ZCShARY5jh8ckw79vXEnOyKVp0dPjmW3IUBOaClwUyXppbNdHtKppSnrVwul7761a9e835/9md/poaGBn3pS1+aM5sl+kvTli1bdO+996qhoSFpawUAAAAAAMmRzBZcwamwhgIhTYYicjnzVVHkvOaVu0OBkJEWLNLFq/WHAyGuFjYsGQkwXqPFSbTqbG9rvep8niStDsh+jZXFqvK6EvqerPa6tWV1scFV4XIZ/Q3ylre8RW95y1v03HPP6dlnn1UgEFBxcbFqa2u1YsWKdC8PAAAAAAAskskKhMbKkoTnPkyGIkbWEzVhOB5IgGUK5t4AyWdZlvY012jHvp5F7TVXQZ52N1ezx5IsK75BbrjhBt1www3pXgYAAAAAADDAdAVC1+nz+vSPfzXvFb7RuQ+dfaOq8rq0p7nmiqvmXc58I+uJchuOBxJgmSKZVWcAXlbn86htW23cVWKugjy1baulOiwFUj5zBQAAAAAALG2mKxDuae9NeO5DRZFT+YYu8HXkWVpR5DQTDLNIgGUG5t4AqbN1Tan2ttaryuuK6f7VXrf2ttZr65rS5C4MkkiuAAAAAACAFDNdgXAhvLi5D73+4OzfeQodalpXbmQ9TTVltJtKAhJg6ZeMuTcAFlbn8+ix7Zv04B0bdMv68is+Bx15lm5dX64H79ig9u0bqVhJIb7pAQAAAABASpmuQFiMq819aGnwqbMv8RPHLZt9CcfAlaIJMBOvEQmwxWHuDZAelmWpsbJEjZUlCk6FNRwIaSIUkduZrxVFTvZRmvCvDgAAAAAAUipagWDqJO1iXT73obGyWFVeV0LzJKq9bm1ZXWxqibgMCbD0Yu4NslVwKqyhQEiToYhcznxVZHFCwlPoyNq15xqjr8J3vvMdk+Hi8ra3vS1txwYAAAAAALEzWYGQqPZu/2xyxbIs7Wmu0Y59PXEND45yFeRpd3P1bCUMzCMBll7MvUE2sW1bR06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", 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" ] }, "metadata": { "image/png": { "height": 611, "width": 811 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "ax.scatter(df.std_range, df.std_logratio, color=blue)\n", "\n", "ax.set_xticklabels([])\n", "ax.set_xlabel(\"Standardized range\")\n", "\n", "ax.set_yticklabels([])\n", "ax.set_ylabel(\"Standardized log ratio\");" ] }, { "cell_type": "markdown", "metadata": { "id": "2mYwxtSfg8LE" }, "source": [ "This data set has a two interesting properties that make it useful for illustrating dependent density regression.\n", "\n", "1. The relationship between range and log ratio is nonlinear, but has locally linear components.\n", "2. The observation noise is [heteroskedastic](https://en.wikipedia.org/wiki/Heteroscedasticity); that is, the magnitude of the variance varies with the range.\n", "\n", "The intuitive idea behind dependent density regression is to reduce the problem to many (related) density estimates, conditioned on fixed values of the predictors. The following animation illustrates this intuition." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "di7x_3pvg8LE" }, "outputs": [], "source": [ "fig, (scatter_ax, hist_ax) = plt.subplots(ncols=2, figsize=(16, 6))\n", "\n", "scatter_ax.scatter(df.std_range, df.std_logratio, color=blue, zorder=2)\n", "\n", "scatter_ax.set_xticklabels([])\n", "scatter_ax.set_xlabel(\"Standardized range\")\n", "\n", "scatter_ax.set_yticklabels([])\n", "scatter_ax.set_ylabel(\"Standardized log ratio\")\n", "\n", "bins = np.linspace(df.std_range.min(), df.std_range.max(), 25)\n", "\n", "hist_ax.hist(df.std_logratio, bins=bins, color=\"k\", lw=0, alpha=0.25, label=\"All data\")\n", "\n", "hist_ax.set_xticklabels([])\n", "hist_ax.set_xlabel(\"Standardized log ratio\")\n", "\n", "hist_ax.set_yticklabels([])\n", "hist_ax.set_ylabel(\"Frequency\")\n", "\n", "hist_ax.legend(loc=2)\n", "\n", "endpoints = np.linspace(1.05 * df.std_range.min(), 1.05 * df.std_range.max(), 15)\n", "\n", "frame_artists = []\n", "\n", "for low, high in zip(endpoints[:-1], endpoints[2:]):\n", " interval = scatter_ax.axvspan(low, high, color=\"k\", alpha=0.5, lw=0, zorder=1)\n", " *_, bars = hist_ax.hist(\n", " df[df.std_range.between(low, high)].std_logratio, bins=bins, color=\"k\", lw=0, alpha=0.5\n", " )\n", "\n", " frame_artists.append((interval,) + tuple(bars))\n", "\n", "animation = ani.ArtistAnimation(fig, frame_artists, interval=500, repeat_delay=3000, blit=True)\n", "plt.close()\n", "# prevent the intermediate figure from showing" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 641 }, "id": "SyWtHa72g8LE", "outputId": "c48bcfec-aa82-41ec-ce9a-32667117125e" }, "outputs": [ { "data": { "text/html": [ "

" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "\n", "HTML(animation.to_html5_video())" ] }, { "cell_type": "markdown", "metadata": { "id": "i3B2R7-vg8LE" }, "source": [ "As we slice the data with a window sliding along the x-axis in the left plot, the empirical distribution of the y-values of the points in the window varies in the right plot. An important aspect of this approach is that the density estimates that correspond to close values of the predictor are similar.\n", "\n", "In the previous example, we saw that a Dirichlet process estimates a probability density as a mixture model with infinitely many components. In the case of normal component distributions,\n", "\n", "$$y \\sim \\sum_{i = 1}^{\\infty} w_i \\cdot N(\\mu_i, \\tau_i^{-1}),$$\n", "\n", "where the mixture weights, $w_1, w_2, \\ldots$, are generated by a [stick-breaking process](https://en.wikipedia.org/wiki/Dirichlet_process#The_stick-breaking_process).\n", "\n", "Dependent density regression generalizes this representation of the Dirichlet process mixture model by allowing the mixture weights and component means to vary conditioned on the value of the predictor, $x$. That is,\n", "\n", "$$y\\ |\\ x \\sim \\sum_{i = 1}^{\\infty} w_i\\ |\\ x \\cdot N(\\mu_i\\ |\\ x, \\tau_i^{-1}).$$\n", "\n", "In this example, we will follow Chapter 23 of [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/) and use a probit stick-breaking process to determine the conditional mixture weights, $w_i\\ |\\ x$. The probit stick-breaking process starts by defining\n", "\n", "$$v_i\\ |\\ x = \\Phi(\\alpha_i + \\beta_i x),$$\n", "\n", "where $\\Phi$ is the cumulative distribution function of the standard normal distribution. We then obtain $w_i\\ |\\ x$ by applying the stick breaking process to $v_i\\ |\\ x$. That is,\n", "\n", "$$w_i\\ |\\ x = v_i\\ |\\ x \\cdot \\prod_{j = 1}^{i - 1} (1 - v_j\\ |\\ x).$$\n", "\n", "For the LIDAR data set, we use independent normal priors $\\alpha_i \\sim N(0, 5^2)$ and $\\beta_i \\sim N(0, 5^2)$. We now express this model for the conditional mixture weights using `PyMC`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "5EgbxpkUg8LE" }, "outputs": [], "source": [ "def norm_cdf(z):\n", " return 0.5 * (1 + pt.erf(z / np.sqrt(2)))\n", "\n", "\n", "def stick_breaking(v):\n", " return v * pt.concatenate(\n", " [pt.ones_like(v[:, :1]), pt.extra_ops.cumprod(1 - v[:, :-1], axis=1)], axis=1\n", " )" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "qtZS8sing8LE" }, "outputs": [], "source": [ "N = len(df)\n", "K = 20\n", "\n", "std_range = df.std_range.values\n", "std_logratio = df.std_logratio.values\n", "\n", "with pm.Model(coords={\"N\": np.arange(N), \"K\": np.arange(K) + 1}) as model:\n", " alpha = pm.Normal(\"alpha\", 0, 5, dims=\"K\")\n", " beta = pm.Normal(\"beta\", 0, 5, dims=\"K\")\n", " x = pm.Data(\"x\", std_range, dims=\"N\")\n", " v = norm_cdf(alpha + pt.outer(x, beta))\n", " w = pm.Deterministic(\"w\", stick_breaking(v), dims=[\"N\", \"K\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "TKt9RzIVg8LF" }, "source": [ "We have defined `x` as a `pm.Data` container in order to use `PyMC`'s posterior prediction capabilities later.\n", "\n", "While the dependent density regression model theoretically has infinitely many components, we must truncate the model to finitely many components (in this case, twenty) in order to express it using `PyMC`. After sampling from the model, we will verify that truncation did not unduly influence our results.\n", "\n", "Since the LIDAR data seems to have several linear components, we use the linear models\n", "\n", "$$\n", "\\begin{align*}\n", "\\mu_i\\ |\\ x\n", " & \\sim \\gamma_i + \\delta_i x \\\\\n", "\\gamma_i\n", " & \\sim N(0, 10^2) \\\\\n", "\\delta_i\n", " & \\sim N(0, 10^2)\n", "\\end{align*}\n", "$$\n", "\n", "for the conditional component means." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "qMLOhLHsg8LF" }, "outputs": [], "source": [ "with model:\n", " gamma = pm.Normal(\"gamma\", 0, 3, dims=\"K\")\n", " delta = pm.Normal(\"delta\", 0, 3, dims=\"K\")\n", " mu = pm.Deterministic(\"mu\", gamma + pt.outer(x, delta), dims=(\"N\", \"K\"))" ] }, { "cell_type": "markdown", "metadata": { "id": "4dcBWBbvg8LF" }, "source": [ "Finally, we specify a `NormalMixture` likelihood function, using the weights we have modeled above." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "ag8Lwc9sg8LF", "outputId": "85b8d803-d144-4073-8e5d-7f3ffd35e48a" }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with model:\n", " sigma = pm.HalfNormal(\"sigma\", 3, dims=\"K\")\n", " y = pm.Data(\"y\", std_logratio, dims=\"N\")\n", " obs = pm.NormalMixture(\"obs\", w, mu, sigma=sigma, observed=y, dims=\"N\")\n", "\n", "pm.model_to_graphviz(model)" ] }, { "cell_type": "markdown", "metadata": { "id": "gUPThEEEg8LF" }, "source": [ "We now sample from the dependent density regression model using a Metropolis sampler. The default NUTS sampler has a difficult time sampling the stick-breaking model, so we will employ a `CompoundSampler`, using a slice sampler for `alpha` and `beta` while leaving NUTS for the rest of the parameters." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70, "referenced_widgets": [ "e2c19d27c2d24df69b2570d2580009a1", "6d10b9e9b680495386f1803d8994c2fb" ] }, "id": "FSYdNHFUg8LF", "outputId": "829d4ee8-c971-4962-aa71-265f93eeb356" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Multiprocess sampling (2 chains in 2 jobs)\n", "CompoundStep\n", ">CompoundStep\n", ">>Slice: [alpha]\n", ">>Slice: [beta]\n", ">NUTS: [gamma, delta, sigma]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bf91e762a96e4174b5623cda135efddd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Sampling 2 chains for 5_000 tune and 1_000 draw iterations (10_000 + 2_000 draws total) took 299 seconds.\n", "We recommend running at least 4 chains for robust computation of convergence diagnostics\n", "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n", "The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n" ] } ], "source": [ "with model:\n", " trace = pm.sample(random_seed=SEED, step=pm.Slice([alpha, beta]), tune=5_000, cores=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see from the R-hat diagnostics below (all near 1.0) that the model is reasonably well converged." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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	mean	sd	hdi_3%	hdi_97%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
beta[1]	6.657	2.228	3.317	10.882	0.196	0.139	143.0	375.0	1.04
beta[2]	9.232	2.502	5.060	14.044	0.068	0.050	1462.0	1403.0	1.00
beta[3]	-2.719	3.672	-10.514	3.325	0.113	0.080	1061.0	1204.0	1.00
beta[4]	0.070	5.044	-9.227	10.007	0.111	0.108	2050.0	1481.0	1.00
beta[5]	-0.032	4.961	-9.759	8.799	0.119	0.113	1725.0	1400.0	1.00
beta[6]	-0.092	5.142	-9.506	9.342	0.117	0.136	1942.0	1313.0	1.00
beta[7]	-0.145	4.993	-8.767	9.865	0.113	0.104	1951.0	1416.0	1.00
beta[8]	-0.120	4.882	-8.992	8.987	0.108	0.100	2072.0	1409.0	1.00
beta[9]	0.190	5.001	-9.486	9.327	0.114	0.121	1923.0	1244.0	1.00
beta[10]	0.026	4.926	-9.552	8.820	0.110	0.112	2022.0	1635.0	1.00
beta[11]	-0.057	4.936	-9.251	9.237	0.120	0.112	1707.0	1460.0	1.00
beta[12]	-0.044	5.095	-9.652	9.425	0.124	0.115	1709.0	1399.0	1.00
beta[13]	-0.046	4.874	-8.849	9.447	0.105	0.110	2141.0	1500.0	1.00
beta[14]	0.016	4.910	-9.891	8.236	0.140	0.111	1245.0	1303.0	1.00
beta[15]	-0.117	4.847	-8.594	9.390	0.112	0.108	1876.0	1510.0	1.00
beta[16]	-0.027	5.003	-8.753	9.791	0.116	0.116	1863.0	1496.0	1.00
beta[17]	-0.169	5.087	-9.898	8.967	0.119	0.118	1828.0	1523.0	1.00
beta[18]	0.225	4.990	-9.447	9.367	0.112	0.111	1986.0	1425.0	1.00
beta[19]	-0.007	4.885	-9.784	8.684	0.110	0.111	1966.0	1533.0	1.00
beta[20]	-0.013	4.912	-9.668	8.658	0.111	0.110	1956.0	1533.0	1.00

\n", "

" ], "text/plain": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk \\\n", "beta[1] 6.657 2.228 3.317 10.882 0.196 0.139 143.0 \n", "beta[2] 9.232 2.502 5.060 14.044 0.068 0.050 1462.0 \n", "beta[3] -2.719 3.672 -10.514 3.325 0.113 0.080 1061.0 \n", "beta[4] 0.070 5.044 -9.227 10.007 0.111 0.108 2050.0 \n", "beta[5] -0.032 4.961 -9.759 8.799 0.119 0.113 1725.0 \n", "beta[6] -0.092 5.142 -9.506 9.342 0.117 0.136 1942.0 \n", "beta[7] -0.145 4.993 -8.767 9.865 0.113 0.104 1951.0 \n", "beta[8] -0.120 4.882 -8.992 8.987 0.108 0.100 2072.0 \n", "beta[9] 0.190 5.001 -9.486 9.327 0.114 0.121 1923.0 \n", "beta[10] 0.026 4.926 -9.552 8.820 0.110 0.112 2022.0 \n", "beta[11] -0.057 4.936 -9.251 9.237 0.120 0.112 1707.0 \n", "beta[12] -0.044 5.095 -9.652 9.425 0.124 0.115 1709.0 \n", "beta[13] -0.046 4.874 -8.849 9.447 0.105 0.110 2141.0 \n", "beta[14] 0.016 4.910 -9.891 8.236 0.140 0.111 1245.0 \n", "beta[15] -0.117 4.847 -8.594 9.390 0.112 0.108 1876.0 \n", "beta[16] -0.027 5.003 -8.753 9.791 0.116 0.116 1863.0 \n", "beta[17] -0.169 5.087 -9.898 8.967 0.119 0.118 1828.0 \n", "beta[18] 0.225 4.990 -9.447 9.367 0.112 0.111 1986.0 \n", "beta[19] -0.007 4.885 -9.784 8.684 0.110 0.111 1966.0 \n", "beta[20] -0.013 4.912 -9.668 8.658 0.111 0.110 1956.0 \n", "\n", " ess_tail r_hat \n", "beta[1] 375.0 1.04 \n", "beta[2] 1403.0 1.00 \n", "beta[3] 1204.0 1.00 \n", "beta[4] 1481.0 1.00 \n", "beta[5] 1400.0 1.00 \n", "beta[6] 1313.0 1.00 \n", "beta[7] 1416.0 1.00 \n", "beta[8] 1409.0 1.00 \n", "beta[9] 1244.0 1.00 \n", "beta[10] 1635.0 1.00 \n", "beta[11] 1460.0 1.00 \n", "beta[12] 1399.0 1.00 \n", "beta[13] 1500.0 1.00 \n", "beta[14] 1303.0 1.00 \n", "beta[15] 1510.0 1.00 \n", "beta[16] 1496.0 1.00 \n", "beta[17] 1523.0 1.00 \n", "beta[18] 1425.0 1.00 \n", "beta[19] 1533.0 1.00 \n", "beta[20] 1533.0 1.00 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "az.summary(trace, var_names=[\"beta\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "io6KXPdgg8LF" }, "source": [ "To verify that truncation did not unduly influence our results, we plot the largest posterior expected mixture weight for each component. (In this model, each point has a mixture weight for each component, so we plot the maximum mixture weight for each component across all data points in order to judge if the component exerts any influence on the posterior.)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628 }, "id": "L_yuCm6Fg8LF", "outputId": "dda7fd9e-b609-4a23-8dc1-d298353c7182" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "

" ] }, "metadata": { "image/png": { "height": 611, "width": 811 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "max_mixture_weights = trace.posterior[\"w\"].mean((\"chain\", \"draw\")).max(\"N\")\n", "ax.bar(max_mixture_weights.coords.to_index(), max_mixture_weights)\n", "\n", "ax.set_xlim(1 - 0.5, K + 0.5)\n", "ax.set_xticks(np.arange(0, K, 2) + 1)\n", "ax.set_xlabel(\"Mixture component\")\n", "\n", "ax.set_ylabel(\"Largest posterior expected\\nmixture weight\");" ] }, { "cell_type": "markdown", "metadata": { "id": "6Pq0WqBbg8LF" }, "source": [ "Since only six mixture components have appreciable posterior expected weight for any data point, we can be fairly certain that truncation did not unduly influence our results. (If most components had appreciable posterior expected weight, truncation may have influenced the results, and we would have increased the number of components and sampled again.)\n", "\n", "Visually, it is reasonable that the LIDAR data has three linear components, so these posterior expected weights seem to have identified the structure of the data well. We now sample from the posterior predictive distribution to get a better understand the model's performance." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 33, "referenced_widgets": [ "64628338cd314dcf998fdcdec5e64a2c", "8283e2190c4d45a6926da9d95273d376" ] }, "id": "-tAIHunXg8LF", "outputId": "733df6c3-aa98-44b6-bace-cc2075cee2a9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [obs]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7a7d1015fe7e4c1cb44c0cb13d0f73b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

\n" ], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lidar_pp_x = np.linspace(std_range.min() - 0.05, std_range.max() + 0.05, 100)\n", "\n", "with model:\n", " pm.set_data(\n", " {\"x\": lidar_pp_x, \"y\": np.zeros_like(lidar_pp_x)}, coords={\"N\": np.arange(len(lidar_pp_x))}\n", " )\n", "\n", " pm.sample_posterior_predictive(\n", " trace, predictions=True, extend_inferencedata=True, random_seed=SEED\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "UecH3-jAg8LF" }, "source": [ "Below we plot the posterior expected value and the 95% posterior credible interval." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628 }, "id": "m2ZWtSuQg8LF", "outputId": "ade722fc-744c-4b8a-8bf6-ec4fe55ce657" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 611, "width": 811 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "ax.scatter(df.std_range, df.std_logratio, color=blue, zorder=10, label=None)\n", "\n", "low, high = np.percentile(az.extract(trace.predictions)[\"obs\"].T, [2.5, 97.5], axis=0)\n", "ax.fill_between(\n", " lidar_pp_x, low, high, color=\"k\", alpha=0.35, zorder=5, label=\"95% posterior credible interval\"\n", ")\n", "\n", "ax.plot(\n", " lidar_pp_x,\n", " trace.predictions[\"obs\"].mean((\"chain\", \"draw\")).values,\n", " c=\"k\",\n", " zorder=6,\n", " label=\"Posterior expected value\",\n", ")\n", "\n", "ax.set_xticklabels([])\n", "ax.set_xlabel(\"Standardized range\")\n", "\n", "ax.set_yticklabels([])\n", "ax.set_ylabel(\"Standardized log ratio\")\n", "\n", "ax.legend(loc=1)\n", "ax.set_title(\"LIDAR Data\");" ] }, { "cell_type": "markdown", "metadata": { "id": "0vFYLTpZg8LF" }, "source": [ "The model has fit the linear components of the data well, and also accommodated its heteroskedasticity. This flexibility, along with the ability to modularly specify the conditional mixture weights and conditional component densities, makes dependent density regression an extremely useful nonparametric Bayesian model.\n", "\n", "To learn more about dependent density regression and related models, consult [_Bayesian Data Analysis_](http://www.stat.columbia.edu/~gelman/book/), [_Bayesian Nonparametric Data Analysis_](http://www.springer.com/us/book/9783319189673), or [_Bayesian Nonparametrics_](https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=bayesian+nonparametrics+book).\n", "\n", "This example first appeared [here](http://austinrochford.com/posts/2017-01-18-ddp-pymc3.html)." ] }, { "cell_type": "markdown", "metadata": { "id": "CxDFNZDtg8LF" }, "source": [ "## Authors\n", "* authored by Austin Rochford in 2017\n", "* updated to PyMC v5 by Christopher Fonnesbeck in September 2024" ] }, { "cell_type": "markdown", "metadata": { "id": "e41HT-6Og8LF" }, "source": [ "## References\n", "\n", ":::{bibliography}\n", ":filter: docname in docnames\n", ":::" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NMqJeLTAg8LF", "outputId": "2a8b67c1-2922-4aff-82b2-392d66190951" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last updated: Mon Sep 30 2024\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.5\n", "IPython version : 8.27.0\n", "\n", "seaborn : 0.13.2\n", "IPython : 8.27.0\n", "requests : 2.32.3\n", "matplotlib: 3.9.2\n", "pymc : 5.16.2\n", "numpy : 1.26.4\n", "arviz : 0.19.0\n", "pandas : 2.2.2\n", "pytensor : 2.25.4\n", "\n", "Watermark: 2.5.0\n", "\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -n -u -v -iv -w" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.5" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "64628338cd314dcf998fdcdec5e64a2c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_8283e2190c4d45a6926da9d95273d376", "msg_id": "", "outputs": [ { "data": { "text/html": "

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