## Tutorial: Graphical Models and Bayesian Networks
with R

**Søren Højsgaard**,
Department of Moelcular Biology and Genetics, Aarhus University, Denmark.

### Goals

Introduce participants to using R for working with graphical
models (in particular graphical log-linear models for discrete data (contingency
tables)) and to probability propagation in Bayesian networks.
### Outline

There will be a running example about building a
probabilistic expert system for a medical diagnosis from real-world data.

- Probability propagation with Bayesian networks (BNs) and their implementation in the
`gRain` (gRaphical independence networks)
package. - A look under the hood of BNs to understand mechanisms of probability propagation.
- Dependency graphs and conditional independence restrictions.
- Log-linear models, graphical models, decompsable models and their implementation in the
`gRim` (gRaphical independence models) package. - Model selection with
`gRim` - Converting a decompsable graphical model to a Bayesian network.

### Prerequisites

Attendees are assumed to have a
working understanding of log-linear models for contingency tables.
### Further Information

Lecture slides etc. will be handed out during the tutorial. The material will also be made available from the instructors homepage. The instructor is currently (together with Steffen
Lauritzen, Univ. of Oxford, and David Edwards, Aarhus Univ.) writing the book
"Graphical Models with R" which is scheduled to be published by Springer in the
spring 2011 in the useR! series.

Please check here for up to date tutorial resources.