{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "import operator as op\n", "import functools as ft\n", "\n", "def summ(a_map):\n", " \"sum(map) does not do what one would expect it to do, but summ(map) does\"\n", " return ft.reduce(op.add, a_map)\n", "\n", "def integr(acc, new):\n", " \"reduction function to compute an integral\"\n", " if not acc: \n", " acc = [0]\n", " last = 0\n", " else:\n", " last = acc[-1]\n", " \n", " acc.append(last+new)\n", " return acc\n", " \n", "def pf(Proba):\n", " \"converting uniform draw into binary draw (factory function)\"\n", " def f(x):\n", " return 0 if x>P else 1\n", " f.__doc__ = \"converts uniform draw into binary draw with proba %.4f\" % Proba\n", " return f\n", "\n", "from scipy.misc import comb\n", "def pdf(n,p,k):\n", " \"the binomial density function B(n,p,k)\"\n", " return comb(n, k) * p**k * (1-p)**(n-k)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gaussf(mu=0.0,sig=1.0):\n", " \"factory function, returning an unnormalised Gaussian\"\n", " def _gaussf(x):\n", " z = (x - mu)/sig\n", " return exp(-z*z)\n", " \n", " _gaussf.__doc__ = \"unnormalised Gaussian (mu=%f, sig=%f)\" % (mu,sig)\n", " return _gaussf\n", "\n", "def constf(c=1.0):\n", " \"factory function, returning an constant function\"\n", " def _constf(x):\n", " return c + 0 * x\n", "\n", " _constf.__doc__ = \"constant function (c=%f)\" % (c)\n", " return _constf\n", "\n", "def normalised(y, x):\n", " \"returns normalised y-vector (unity integral, over x; x assumed to be equally spaced)\"\n", " dx = x[1] - x[0]\n", " s = sum(y) * dx\n", " return y / s\n", "\n", "#f = constf()\n", "#g = gaussf()\n", "#help(g)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class bayes2:\n", " \"\"\"simple class allowing to compute Bayesian combination of two measurements\n", " \n", " DESCRIPTION\n", " \n", " the following properties are used to construct the data\n", "\n", " min, max, n - support area (and grid steps)\n", " mu1, sig1 - first measurement\n", " mu2, sig2 - second measurement\n", "\n", " title, xlabel, ylabel - graph labels\n", " \n", " the function draw() then draws it\n", " \n", " DEPENDENCIES\n", " \n", " normalised()\n", " gaussf()\n", " constf()\n", " \"\"\"\n", "\n", " \n", " def __init__(self):\n", " self.n = 1000\n", " \n", " self.mu1 = 5\n", " self.sig1 = 1\n", " self.mu2 = 6\n", " self.sig2 = 2\n", " \n", " self.min = 0.\n", " self.max = 10.\n", " \n", " self.title = 'Measurements and Bayesian post density function'\n", " self.xlabel = 'probability space location'\n", " self.ylabel = 'probability density'\n", " \n", " def draw(self):\n", " x = np.linspace(self.min, self.max, 1000)\n", " prior = constf(0.5)(x)\n", " f1 = normalised(gaussf(self.mu1,self.sig1)(x),x)\n", " f2 = normalised(gaussf(self.mu2,self.sig2)(x),x)\n", " ff = normalised(f1 * f2,x)\n", " #plt.plot(x,prior, label='prior')\n", " plt.plot(x,f1, 'k--', label='m1')\n", " plt.plot(x,f2, 'k--', label='m2')\n", " plt.plot(x,ff, 'r-', label='post')\n", " plt.title(self.title)\n", " plt.xlabel(self.xlabel)\n", " plt.ylabel(self.ylabel)\n", " plt.legend()\n", " plt.show()\n", "\n", "b = bayes2()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Frequentist vs Bayesian statistics - Part II" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We know that in a Bayesian setting the probability of our hypothesis conditional on our data is\n", "$$\n", "P(H|D) \\propto P(D|H) \\times P(H)\n", "$$\n", "where $P(H)$ is the prior, and $P(D|H)$ is the probability of our data conditional on the hypothesis. \n", "\n", "What we are looking at now is a situation where we have two measurements for the same quantity, say two people measuring a length. One of the measurements is $\\mu_1, \\sigma_1$ (in the sense that the measurement we've got is $\\mu_1$, and the measurement carries a Gaussian error of standard deviation $\\sigma_1$), and the other one is $\\mu_2, \\sigma_2$.\n", "\n", "In principle we are starting with a flat prior $P(H)$*, that we are then updating with the first measurement to obtain our new prior $P_1(H) = P(H|D1)$, that in turn we are updating with the second measurement. \n", "$$\n", "P(H|D_1, D_2) \\propto P(D_2|H) \\times P_1(H) \\propto P(D_2|H) \\times P(D_1|H) \\times P(H)\n", "$$\n", "However, because the first prior is flat we can simply use $P(D_1|H)$ as our first and only prior.\n", "\n", "*note: in fact we are starting not with a flat prior, but with a prior that has support $[min\\ldots max]$ by virtue of the fact that we are not evaluating the functions outside of this area; this is one of the rather nice properties of this calculus: restricting the calculation area to an interval is a feature" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ia88e+PlnaNbswKxRo0bx22+/Fd0XUTGNHj2aunXr5p/ZsqUlBJNUoikhTBWR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\nSZMmdCzNncM7d8I//+m6o9i2zbUdnHB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 5\n", "b.sig1 = 1\n", "b.mu2 = 5\n", "b.sig2 = 1\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">Two equal measurements (same measurement and same error) lead to a more localised posterior function that is otherwise at the same place" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">Two measurements at the same location (same measurement but different error) lead to a posterior function that is at the same place, and thinner (or, at least as thin) as the thinner of the two" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jiD179tCgQQPWr19PUlJS7hlq1IAffoDzzgt5+P333+f1119n+vTpYekYNmwY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\nIAiCwAmDEARBEADwNxc3ZHdjwzHeAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 5\n", "b.sig1 = 1\n", "b.mu2 = 6\n", "b.sig2 = 1\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Two measurements of equal error but at different locations lead to a more localised function in the middle" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BLF++nOOOOy7mKqOU5Lvv4E9/gh9+qNjxxcVQrZpT9VQllnev4DNt2jQGDx7M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\nXBzfjCdMCKZBAEfXhAlxz/bZZ59NmR5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cOZ4/VtUjfZZSYUTkP8BPqnqQh1DKMaNxfvdB\nvZIMIxmx+j8jXiTtm4U4XWK3sv/N3zDSEvMQDMMwDMDaEAzDMAwXMwiGYRgGYAbB+P/t1TEBAAAA\nwqD1T+1jDCgBwAkBgEoIAJwQAKhqfg4dzsWrPe8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 3\n", "b.sig1 = .5\n", "b.mu2 = 7\n", "b.sig2 = .5\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Incongruent measurements lead to a rather pathological behaviour: in this case we have essentially two contradictory measurements of equal error, and they give a strong peak in the middle; this obviously makes sense when thinking about how those things are computed, but it is important to keep in mind that a single _overconfident_ measurement can move things around dramatically" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ncKzayByCYZSLUBzCRPdT0JBsjcpG+QlXCQGcfKxh2TDKTSi9jN4TkXggRVVX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\nu0bsYG0IMcTw4cO57rrrIusMVqyAadP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 3\n", "b.sig1 = .5\n", "b.mu2 = 7\n", "b.sig2 = 1\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Similar effect as before, that incongruent measurements move the posterior distribution dramatically; note however that the more confident density is significantly stronger" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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777zD2LFhXlZLCMaUe5EkhEB14wLgbVX9OYbxGJ+EHZQGpepDeP3115kwYYJ3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\nk8Hq1c6ppr//fXyDi7KWLVty5ZVXhv7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 3\n", "b.sig1 = .5\n", "b.mu2 = 7\n", "b.sig2 = 2\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">Again a similar effect, but here the confident density mostly dominates the posterior" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XX02XLl1ITk7mpZfKGhQwfiwhGFOFzX74YWbXrVvyyTp14Nhj4bvv/AnKRKR79+488cQT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\n3Lhx+xOCSTiWEIzxwfbt2+n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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 6\n", "b.sig1 = .5\n", "b.mu2 = 7\n", "b.sig2 = 2\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">Here the two densities are congruent, but one is significantly more confident; the other one has very little impact" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RkRF4ZZMmMGNGMSM05Uk4Vxm9KyLVgZaquiQGMRljPEFrCFu3Qps2YW+nRYsW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\nrOD6MWP4+uuvueuuu2Iy81nUXXyxG85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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b.mu1 = 6\n", "b.sig1 = .5\n", "b.mu2 = 7\n", "b.sig2 = 1\n", "b.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> this is a scenario where the less confident density has some impact" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }