2 Graphical Models in a Nutshell

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2 Graphical Models in a Nutshell Daphne Koller, Nir Friedman, Lise Getoor and Ben Taskar Probabilistic Graphical Models are an elegant framework which combines uncer- tainty (probabilities) and logical structure (independence constraints) to compactly represent complex, real-world phenomena. The framework is quite general in that many of the commonly proposed statistical Models (Kalman lters, hidden Markov Models , Ising Models ) can be described as Graphical Models . Graphical Models have enjoyed a surge of interest in the last two decades, due both to the exibility and power of the representation and to the increased ability to e ectively learn and perform inference in large networks. Introduction Graphical Models [11, 3, 5, 9, 7] have become an extremely popular tool for mod- eling uncertainty.

This chapter provides a compactgraphicalmodels tutorialbased on [8]. We cover representation, inference, and learning. Our tutorial is not comprehensive; for more detailssee[8,11,3,5,9,4,6]. 2.2 Representation The two most common classes of graphical models are Bayesian networks and Markov networks. The underlying semantics of Bayesian networks ...

  Chapter, Representation