**Graphical Models and****
Inference - Lectures 15 and ****16**

**Bayesian Graphical Models**

This lecture describes the general idea behind modern developments in Bayesian statistics, using graphical models as a paradigm in combination with Markov chain Monte Carlo Methods (MCMC) for computation.

The idea is to treat ordinary statistical problems as special
instances of probabilistic expert systems, replacing probability
propagation algorithms with ordinary MCMC. Parameters and data are
explicitly represented in the network and
*plates are used to indicate repetition patterns.*

Bayesian graphical models are very well described in the manual to WinBUGS.

Here is a link to a draft chapter in a forthcoming book about Graphical Models in R

**Links**

Last updated: Monday, 28 November 2011. Steffen L. Lauritzen