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2 Graphical Models in a Nutshell
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.
14 Graphical Models in a Nutshell the mechanisms for gluing all these components back together in a probabilistically coherent manner. Effective learning, both parameter estimation and model selec-tion, in probabilistic graphical models is enabled by the compact parameterization. This chapter provides a compactgraphicalmodels tutorialbased on [8].
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