• 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 present other temporal models, such as Markov decision processes (fully and partially observable) and dynamic limited-memory influence diagrams.

The materials for the course (schedule, slides, exercises, bibliography, links, etc.) will be available before its beginning at www.cisiad.uned.es/cursos/SMDM-2020-PGMs.