From 8e76a6b3b1770c250e246d97bcfbce6de7f51bae Mon Sep 17 00:00:00 2001
From: Jeremy Teitelbaum <jeremy.teitelbaum@uconn.edu>
Date: Fri, 27 Apr 2018 14:42:03 -0400
Subject: [PATCH] 5.9.8 added (in progress)

---
 BDA 5.9.8.ipynb | 72 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 BDA 5.9.8.ipynb

diff --git a/BDA 5.9.8.ipynb b/BDA 5.9.8.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Discrete Mixture Models\n",
+    "\n",
+    "Discrete mixture models: if $p_m(\\theta)$, for $m=1,\\ldots,M$ are conjugate prior densities for the sampling model $y|\\theta$, show that the class of finite mixture prior densities given by \n",
+    "$$\n",
+    "p(\\theta)=\\sum_{1}^{M} \\lambda_m p_m(\\theta)\n",
+    "$$\n",
+    "is also a conjugate class, where the $\\lambda_m$’s are nonnegative weights that sum to 1. This can provide a useful extension of the natural conjugate prior family to more flexible distributional forms. As an example, use the mixture form to create a bimodal prior density for a normal mean, that is thought to be near $1$, with a standard deviation of $0.5$, but has a small probability of being near $−1$, with the same standard deviation. If the variance of each observation $y_1,\\ldots,y_{10}$ is known to be $1$, and their observed mean is $y =−0.25$, derive your posterior distribution for the mean, making a sketch of both prior and posterior densities. Be careful: the prior and posterior mixture proportions are different.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's skip the theory part and look at the example.\n",
+    "\n",
+    "We have\n",
+    "$$\n",
+    "p(\\theta|y_1,\\ldots,y_{10})\\propto p(y_1,\\ldots,y_10|\\theta)p(\\theta)$$\n",
+    "so\n",
+    "$$\n",
+    "p(\\theta|\\{y_{i}\\})\\propto \\sum \\lambda_{m}p(\\{y_{i}\\}|\\theta)p_{m}(\\theta)\n",
+    "$$\n",
+    "\n",
+    "Each of the terms $p_{m}(\\theta)p(\\{y_{i}\\}|\\theta)$\n",
+    "is equal to  $p_{m}(\\theta|\\{y_{i}\\})p_{m}(\\{y_{i}\\})$.\n",
+    "\n",
+    "Therefore the total posterior density is a weighted sum\n",
+    "of the individual posteriors:\n",
+    "\n",
+    "$$p(\\theta|\\{y_{i}\\})=\\sum c_{m}p_{m}(\\theta|\\{y_{i}\\})$$\n",
+    "where \n",
+    "$$\n",
+    "c_{m}=\\frac{\\lambda_m p_{m}(\\{y_{i}\\})}{\\sum_{m} \\lambda_m p_{m}(\\{y_{i}\\}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.6.4"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}