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Received:July 17, 2013 Revised:August 02, 2013
Received:July 17, 2013 Revised:August 02, 2013
Abstract:Probabilistic graphical models are powerful tools for compactly representing complex probability distributions, efficiently computing (approximate) marginal and conditional distributions, and conveniently learning parameters and hyperparameters in probabilistic models. As a result, they have been widely used in applications that require some sort of automated probabilistic reasoning, such as computer vision and natural language processing, as a formal approach to deal with uncertainty. This paper surveys the basic concepts and key results of representation, inference and learning in probabilistic graphical models, and demonstrates their uses in two important probabilistic models. It also reviews some recent advances in speeding up classic approximate inference algorithms, followed by a discussion of promising research directions.
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ZHANG Hong-Yi,WANG Li-Wei,CHEN Yu-Xi.Research Progress of Probabilistic Graphical Models:A Survey.Journal of Software,2013,24(11):2476-2497
ZHANG Hong-Yi,WANG Li-Wei,CHEN Yu-Xi.Research Progress of Probabilistic Graphical Models:A Survey.Journal of Software,2013,24(11):2476-2497