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.

perform inference in large networks. 2.1 Introduction Graphicalmodels[11,3,5,9,7]havebecome an extremely popular tool for mod- ... At a high level, our goal is to efficiently represent a joint distribution P over ... Common effect X →Z ←Y: active if and only if either Z or one of Z’s descendants is observed.

  Network, Distribution, Active