4(A,D) (a) (b) Figure Diﬀerent perspectives on probabilistic graphical models: top — the graphical representa- tion; middle — the independencies induced by the graph structure; bottom — the factorization induced by the graph structure. (a) A sample Bayesian network. (b) A sample Markov network. A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. Book: Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman, MIT Press () Required readings for each lecture posted to course website.
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