Graphical Models in a Nutshell
Daphne Koller, Nir Friedman, Lise Getoor, Ben Taskar · The MIT Press eBooks · 2007
Probabilistic graphical models are an elegant framework which combines uncertainty (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 filters, 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 flexibility and power of the representation and to the increased ability to effectively learn and perform inference in large networks. Graphicalmodels[11,3,5,9,7]havebecome an extremely popular tool for modeling uncertainty. They provide a principled approach to dealing with uncertainty through the use of probability theory, and an effective approach to coping with complexity through the use of graph theory. The two most common types of graphical