Research progress of probabilistic graphical models: a survey.

*(Chinese. English summary)*Zbl 1299.68091Summary: Probabilistic graphical models are powerful tools for a compact represention of complex probability distributions, efficient computing (approximate) of marginal and conditional distributions, and convenient learning of parameters and hyperparameters in probabilistic models. As a result, they are widely used in applications that require some sort of automated probabilistic reasoning as a formal approach to deal with uncertainty such as computer vision and natural language processing. 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.