Graphical Models and Bayesian
Networks with R
Tutorial given at the useR!
2014 conference in Los Angeles
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.
There will be a running example about building a
probabilistic expert system for a medical diagnosis from real-world
- Probability propagation with Bayesian networks (BNs)
and their implementation in the gRain (gRaphical independence
- A look under the hood of BNs to
understand mechanisms of probability
- Dependency graphs and conditional
- 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.