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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conjugate Normal Distributions (known variance)\n",
+    "\n",
+    "We are trying to learn about the unknown mean of a normal distribution with known variance.  \n",
+    "We choose  a prior distribution is normal with mean $\\mu_{0}$ and variance $\\tau_{0}^2$. \n",
+    "We draw $n$ values $y_1,\\ldots, y_n$ from the distribution with known variance $\\sigma^2$.  The posterior distribution\n",
+    "$p(\\mu|y_1,\\ldots,y_n)=p(y_1,\\ldots,y_n|\\mu)p(\\mu)$ is again normal.  Let \n",
+    "$$\n",
+    "\\overline{y}=\\frac{1}{n}\\sum_{i=1}^{n} y_i\n",
+    "$$\n",
+    "be the sample mean. \n",
+    "\n",
+    "The posterior variance\n",
+    "is\n",
+    "$$\\frac{1}{\\tau_1^2}=\\frac{1}{\\tau_0^2}+\\frac{n}{\\sigma^2}$$\n",
+    "and the posterior mean is\n",
+    "$$\n",
+    "\\mu_1=\\frac{\\frac{\\mu_0}{\\tau_0^2}+\\frac{n\\overline{y}}{\\sigma^2}}{\\frac{1}{\\tau_{1}^2}}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def posterior(prior_mean,prior_variance,sample_mean,pop_variance,n):\n",
+    "    post_var=1/((1/prior_variance) + n/pop_variance)\n",
+    "    post_mean=(prior_mean/prior_variance+sample_mean*n/pop_variance)/(1/post_var)\n",
+    "    return post_mean, post_var"
+   ]
+  }
+ ],
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