# Gibbs Sampling for the Multivariate Normal

##### Posted on

This note is based on Chapter 7 of Hoff PD. A first course in Bayesian statistical methods. Springer Science & Business Media; 2009 Jun 2.

A $p$-dimensional data vector $\Y$ has a multivariate normal distribution if its sampling density is given by

A convenient prior distribution for the multivariate mean $\btheta$ is a multivariate normal distribution, which can be parameterized as

The joint sampling density of the observed vectors $\y_1,\ldots,y_n$ is

where $\A_1=n\Sigma^{-1},\b_1 = n\Sigma^{-1}\bar \y$. Thus, the posterior distribution of $\btheta$ is

where

Hence,

where

The sum of squares matrix of a collection of multivariate vectors $\z_1,\ldots,\z_n$ is given by

where $\Z$ is the $n\times p$ matrix whose $i$-th row is $\z_i’$.

This suggests the following construction of a “random” covariance matrix: For a given positive integer $\nu_0$ and a $p\times p$ covariance matrix $\Phi_0$,

- sample $\z_1,\ldots,\z_{\nu_0}\sim MN(\0, \Phi_0)$ i.i.d.
- calculate $\Z’\Z=\sum_{i=1}^{\nu_0}\z_i\z_i’$

Repeat this procedure over and over again, generating matrices $\Z_1’\Z_1,\ldots,\Z_S’\Z_S$. The population distribution of these sum of squares matrices is called a **Wishart distribution** with parameters $(\nu_0,\Phi_0)$.

Consider the prior distribution $\Sigma\sim IW(\nu_0,\S_0^{-1})$, the full conditional distribution of $\Sigma$ would be

Hopefully this result seems somewhat intuitive: We can think of $\nu_0+n$ as the “posterior sample size”, being the sum of the “prior sample size” $\nu_0$ and the data sample size.

Now, it is ready to perform Gibbs sampling:

- Sample $\btheta^{s+1}\sim MN(\bmu_n,\Lambda_n)$
- Sample $\Sigma^{s+1}\sim IW(\nu_0+n,\S_n^{-1})$