{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## A Problem that stumped Milton Friedman\n", "\n", "(and that Abraham Wald solved by inventing sequential analysis)\n", "\n", "#### By [Chase Coleman](https://github.com/cc7768) and [Thomas J. Sargent](http://www.tomsargent.com/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by importing some packages called by the code that we will be using in this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.interpolate as interp\n", "import scipy.stats as st\n", "import seaborn as sb\n", "import quantecon as qe\n", "from ipywidgets import interact, widgets\n", "\n", "%matplotlib inline\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A file named Wald_Friedman_utils.py already exists in the specified directory ... skipping download.\n" ] } ], "source": [ "#-Download supporting Wald_Friedman_utils.py file from GitHub-#\n", "qe.fetch_nb_dependencies([\"Wald_Friedman_utils.py\"])\n", "from Wald_Friedman_utils import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sequential analysis\n", "\n", "Key ideas in play are:\n", "\n", " * Bayes' Law\n", " \n", " * Dynamic programming\n", "\n", " * type I and type II statistical errors\n", " \n", " * a type I error occurs when you reject a null hypothesis that is true\n", " \n", " * a type II error is when you accept a null hypothesis that is false \n", " \n", "\n", " * Abraham Wald's **sequential probability ratio test**\n", " \n", " * The **power** of a statistical test\n", " \n", " * The **critical region** of a statistical test\n", " \n", " * A **uniformly most powerful test**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On pages 137-139 of his book **Two Lucky People** with Rose Friedman, Milton Friedman described a problem presented to him and Allen Wallis during World War II when they worked at the U.S. government's Statistical Research Group at Columbia University. \n", "\n", "Let's listen to Milton Friedman tell what happened.\n", "\n", " \"In order to understand the story, it is necessary to have an idea of a simple statistical problem, and of the\n", " standard procedure for dealing with it. The actual problem out of which sequential analysis grew will serve.\n", " The Navy has two alternative designs (say A and B) for a projectile. It wants to determine which is superior. \n", " To do so it undertakes a series of paired firings. On each round it assigns the value 1 or 0 to A accordingly as\n", " its performance is superior or inferior to that of B and conversely 0 or 1 to B. The Navy asks the statistician \n", " how to conduct the test and how to analyze the results. \n", " \n", " \"The standard statistical answer was to specify a number of firings (say 1,000) and a pair of percentages\n", " (e.g., 53% and 47%) and tell the client that if A receives a 1 in more than 53% of the firings, it can be regarded\n", " as superior; if it receives a 1 in fewer than 47%, B can be regarded as superior; if the percentage is between\n", " 47% and 53%, neither can be so regarded.\n", " \n", " \"When Allen Wallis was discussing such a problem with (Navy) Captain Garret L. Schyler, the captain objected that such a test, to quote from Allen's account, may prove wasteful. If a wise and seasoned ordnance officer like Schyler were on the premises, he would see after the first few thousand or even few hundred [rounds] that the experiment need not be completed either because the new method is obviously inferior or because it is obviously superior beyond what was hoped for $\\ldots$ \"\n", " \n", " Friedman and Wallis struggled with the problem but after realizing that they were not able to solve it themselves told Abraham Wald it. That started Wald on the path that led *Sequential Analysis*. We'll formulate the problem using dynamic programming.\n", "\n", "This started Wald on the path that led him to _Sequential Analysis_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamic programming formulation\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following presentation of the problem closely follows Dmitri Berskekas's treatment in **Dynamic Programming and Stochastic Control**. \n", "\n", "An i.i.d. random variable $z$ can take on values \n", "\n", " * $z \\in [ v_1, v_2, \\ldots, v_n]$ when $z$ is a discrete-valued random variable\n", " \n", " * $z \\in V$ when $z$ is a continuous random variable. \n", "\n", "A decision maker wants to know which of two probability distributions governs $z$. To formalize this idea,\n", "let $x \\in [x_0, x_1]$ be a hidden state that indexes the two distributions:\n", "\n", "$$P(v_k \\mid x) = \\begin{cases} f_0(v_k) & \\mbox{if } x = x_0, \\\\\n", " f_1(v_k) & \\mbox{if } x = x_1. \\end{cases}$$\n", " \n", "when $z$ is a discrete random variable and a density\n", "\n", "\n", "$$P(v \\mid x) = \\begin{cases} f_0(v) & \\mbox{if } x = x_0, \\\\\n", " f_1(v) & \\mbox{if } x = x_1. \\end{cases}$$\n", " \n", "when $v$ is continuously distributed. \n", "\n", " \n", "\n", "Before observing any outcomes, a decision maker believes that the probability that $x = x_0$ is $p_{-1}\\in (0,1)$: \n", "\n", "$$p_{-1} = \\textrm{Prob}(x=x_0 \\mid \\textrm{ no observations})$$\n", "\n", "After observing $k+1$ observations $z_k, z_{k-1}, \\ldots, z_0$ he believes that the probability that the distribution is $f_0$ is\n", "\n", "$$p_k = {\\rm Prob} ( x = x_0 \\mid z_k, z_{k-1}, \\ldots, z_0)$$\n", "\n", "We can compute this $p_k$ recursively by applying Bayes' law:\n", "\n", "$$p_0 = \\frac{ p_{-1} f_0(z_0)}{ p_{-1} f_0(z_0) + (1-p_{-1}) f_1(z_0) }$$\n", "\n", "and then\n", "\n", "$$p_{k+1} = \\frac{ p_k f_0(z_{k+1})}{ p_k f_0(z_{k+1}) + (1-p_k) f_1 (z_{k+1}) }.$$\n", "\n", "\n", "After observing $z_k, z_{k-1}, \\ldots, z_0$, the decision maker believes that $z_{k+1}$ \n", "has probability distribution\n", "\n", "$$p(z_{k+1}) = p_k f_0(z_{k+1}) + (1-p_k) f_1 (z_{k+1}).$$\n", "\n", "This is evidently a mixture of distributions $f_0$ and $f_1$, with the weight on $f_0$ being the posterior probability $f_0$ that the distribution is $f_0$. \n", "\n", "**Remark:** *Because the decision maker believes that $z_{k+1}$ is drawn from a mixture of two i.i.d. distributions, he does *not* believe that the sequence $[z_{k+1}, z_{k+2}, \\ldots]$ is i.i.d. Instead, he believes that it is *exchangeable*. See David Kreps\n", "*Notes on the Theory of Choice*, chapter 11, for a discussion.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at some examples of two distributions. Here we'll display two beta distributions. First, we'll show the two distributions, then we'll show mixtures of these same two distributions with various mixing probabilities $p_k$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Create two distributions over 50 values for k\n", "# We are using a discretized beta distribution\n", "p_m1 = np.linspace(0, 1, 50)\n", "f0 = np.clip(st.beta.pdf(p_m1, a=1, b=1), 1e-8, np.inf)\n", "f0 = f0 / np.sum(f0)\n", "f1 = np.clip(st.beta.pdf(p_m1, a=9, b=9), 1e-8, np.inf)\n", "f1 = f1 / np.sum(f1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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