Survey of Probabilistic Graphical Models

Probabilistic graphical model (PGM) is a generic model that represents the probability-based relationships among random variables by a graph, and is a general method for knowledge representation and inference involving uncertainty. In recent years, PGM provides an important means for solving the uncertainty of intelligent information field, and becomes research focus in the fields of machine learning and artificial intelligence etc. In the paper, PGM and its three types of basic models are reviewed, including the learning and inference theory, research status, application and promotion.