# BDA3 Chapter 3 Exercise 9

Here’s my solution to exercise 9, chapter 3, of Gelman’s Bayesian Data Analysis (BDA), 3rd edition. There are solutions to some of the exercises on the book’s webpage.

Suppose we have a normal likelihood $$y \mid \mu, \sigma \sim \dnorm(\mu, \sigma)$$ with conjugate priors

\begin{align} \sigma^2 &\sim \dinvchi(\nu_0, \sigma_0^2) \\ \mu \mid \sigma^2 &\sim \dnorm\left(\mu_0, \frac{\sigma^2}{\kappa_0}\right) . \end{align}

We need to show that the posterior is

$\mu, \sigma^2 \mid y \sim \dnorminvchi\left(\mu_n, \frac{\sigma_n^2}{\kappa_n}, \nu_n, \sigma_n^2\right)$

where

\begin{align} \mu_n &= \frac{\kappa_0}{\kappa_0 + n}\mu_0 + \frac{n}{\kappa_0 + n} \bar y \\ \kappa_n &= \kappa_0 + n \\ \nu_n &= \nu_0 + n \\ \nu_n \sigma_n^2 &= \nu_0 \sigma_0^2 + (n - 1) s^2 + \frac{\kappa_0 n}{\kappa_0 + n}(\bar y - \mu_0)^2 . \end{align}

Using the calculations on pages 67/68, we can compare the factors in front of the exponentials and the exponents of the exponentials, to see that it is sufficient to show that

\begin{align} \frac{1}{\sigma(\sigma^2)^{-(\nu_n / 2 - 1)}} &= \frac{1}{\sigma (\sigma^2)^{-(\nu_0 / 2 + 1)} (\sigma^2)^{-n / 2}} \\ \nu_n \sigma_n^2 + \kappa_n (\mu_n - \mu)^2 &= \nu_0 \sigma_0^2 + \kappa_0 (\mu - \mu_0)^2 + (n - 1)s^2 + n(\bar y - \mu)^2 . \end{align}

The first identity is straight forward so we focus on the second. We will expand the left hand side and drop any terms we find that match those on the right. Expanding the LHS in terms on the hyperpriors, we get

$\nu_0 \sigma_0^2 + (n - 1) s^2 + \frac{\kappa_0 n}{\kappa_0 + n}(\bar y - \mu_0)^2 + (\kappa_0 + n) \left(\frac{\kappa_0}{\kappa_0 + n}\mu_0 + \frac{n}{\kappa_0 + n} \bar y- \mu\right)^2 - \text{RHS} \\ = \frac{\kappa_0 n}{\kappa_0 + n}(\bar y - \mu_0)^2 + (\kappa_0 + n) \left(\frac{\kappa_0}{\kappa_0 + n}\mu_0 + \frac{n}{\kappa_0 + n} \bar y- \mu\right)^2 - \kappa_0 (\mu - \mu_0)^2 - n(\bar y - \mu)^2 .$

Moving the $$\kappa_0 + n$$ denominator of the second term out of the brackets we obtain

$\frac{\kappa_0 n}{\kappa_0 + n}(\bar y - \mu_0)^2 + \frac{1}{(\kappa_0 + n)} \left(\kappa_0\mu_0 + n \bar y- (\kappa_0 + n)\mu\right)^2 - \kappa_0 (\mu - \mu_0)^2 - n(\bar y - \mu)^2 .$

Simplifying and multiplying out the brackets of the second term gives

\begin{align} \left(\kappa_0\mu_0 + n \bar y- (\kappa_0 + n)\mu\right)^2 &= \left( \kappa_0 (\mu_0 - \bar y) + (\kappa_0 + n)(\bar y - \mu) \right)^2 \\ &= \kappa_0^2 (\bar y - \mu_0)^2 + (\kappa_0 + n)^2 (\bar y - \mu)^2 + 2\kappa_0(\kappa_0 + n)(\bar y - \mu)(\mu_0 - \bar y) . \end{align}

Substituting this back in, we can combine the first terms of each and multiply out all the brackets to get

\begin{align} \frac{\kappa_0 n}{\kappa_0 + n}(\bar y - \mu_0)^2 + \frac{1}{\kappa_0 + n} \left( \kappa_0^2 (\bar y - \mu_0)^2 + (\kappa_0 + n)^2 (\bar y - \mu)^2 + 2\kappa_0 (\kappa_0 + n) (\bar y - \mu) (\mu_0 - \bar y) \right) \\ - \kappa_0 (\mu - \mu_0)^2 - n(\bar y - \mu)^2 \\ = \kappa_0 (\bar y - \mu_0)^2 + (\kappa_0 + n) (\bar y - \mu)^2 + 2\kappa_0 (\bar y - \mu) (\mu_0 - \bar y) \\ - \kappa_0 (\mu - \mu_0)^2 - n(\bar y - \mu)^2 \\ = \color{red}{ \kappa_0 \bar y^2 } + \color{blue}{\kappa_0 \mu_0^2} - \color{green}{2\kappa_0 \mu_0 \bar y} + \color{red}{\kappa_0 \bar y^2} + \color{orange}{\kappa_0 \mu^2} - \color{black}{2 \kappa_0 \mu \bar y} + \color{green}{2 \kappa_0 \mu_0 \bar y} \\ + \color{black}{2 \kappa_0 \mu \bar y} - \color{purple}{2 \kappa_0 \mu_0 \mu} - \color{red}{2 \kappa_0 \bar y^2} - \color{orange}{\kappa_0 \mu^2} - \color{blue}{\kappa_0 \mu_0^2} + \color{purple}{2 \kappa_0 \mu_0 \mu} , \end{align}

which cancel to 0.