## Tutorial:
Graphical Models and Bayesian
Networks with R

Tutorial given at the useR!
2014 conference in Los Angeles

**Søren Højsgaard**,
Department of Mathematical Sciences, Aalborg 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