Graphical Models in a Nutshell

Daphne Koller, Nir Friedman, Lise Getoor, Ben Taskar

Research output: Chapter in Book/Report/Conference proceedingChapter


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.
Original languageEnglish
Title of host publicationIntroduction to Statistical Relational Learning
EditorsLise Getoor, Ben Taskar
PublisherThe MIT Press
Number of pages43
ISBN (Electronic)9780262256230
StatePublished - 31 Aug 2007

Publication series

Name Adaptive computation and machine learning


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