The purpose of this course is to introduce probabilistic graphical models and illustrate, by means of hands-on exercises, how to apply them to medical decision analysis. In particular, participants will learn how to:

• build Bayesian networks for medical diagnosis and compare them with naive Bayes models;
• build decision analysis networks for unicriterion and cost-effectiveness analysis and compare them with decision trees;
• build Markov influence diagrams for cost-effectiveness analysis and compare them with spreadsheets, Markov decision trees, and discrete event simulation.

The course will also mention other temporal models, such as Markov decision processes (fully and partially observable) and dynamic limited-memory influence diagrams.

The exercises will be done with OpenMarkov, an open-source software package available at www.openmarkov.org.