F. J. Díez. Probabilistic graphical models in artificial intelligence and medicine. Massachusetts Institute of Technology. Boston, October 13, 2015.
88 slides (44 pages). PDF (1.7 MB).
In the first decades of artificial intelligence, many researchers doubted or denied that probability could play a significant role in this field. The situation changed with the advent of probabilistic graphical models, such as Bayesian networks, influence diagrams, and factored Markov decision processes. Nowadays these models are more and more used in all the fields of artificial intelligence, especially in medicine, because of their decision-theoretic foundation and their ability to combine expert knowledge with data. This seminar will offer an overview of probabilistic graphical models and show several networks for complex medical problems built at the Center for Intelligent Decision-Support Systems (CISIAD) of the National University for Distance Education (UNED) in Madrid, Spain, ranging from medical diagnosis to cost-effectiveness analysis. It will also present an open-source software package for probabilistic graphical models, OpenMarkov, built at the same center.