Dates:
November 3rd + 4th 2016 in Zurich
Lecturer:
Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark
Course description:
Introduce participants to working with Graphical Models (GMs) and Bayesian Networks (BNs) in R. This includes probability propagation in BNs and aspects of learning BNs from data using graphical models.
Topics will include:
Probability propagation with Bayesian networks (BNs) and their implementation in the gRain package.
A look under the hood of BNs to understand mechanisms of probability propagation.
Dependency graphs and conditional independence restrictions.
Learning BNs from data using graphical log-linear models in the gRim package.
Examples from genetics will be used throughout for illustrative purposes. Moreover, there will be a running example about building a BN for a medical diagnosis from real-world data.
Prerequisites:
Attendees are assumed to have a working understanding of log-linear models for contingency tables.
Literature:
Højsgaard, S.; Edwards, D.; Lauritzen, S. (2012): Graphical models with R, Springer
Additional material
Lecture slides can be found here
Additional material: zurich-2017.R; zurich-2017.html
See here for software installation instructions.
After the tutorial
Feb 2017: Notes for the tutorial have been updated due to changes in the R packages.
Feb 2017: Please go to github for devel versions of the packages.