Title :
Survey of Probabilistic Graphical Models
Author :
Li Hongmei ; Hao Wenning ; Gan Wenyan ; Chen Gang
Author_Institution :
Inst. of Command Inf. Syst., PLA Univ. of Sci. & Tech., Nanjing, China
Abstract :
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.
Keywords :
graph theory; inference mechanisms; knowledge representation; probability; random processes; PGM; inference theory; intelligent information field; knowledge representation; probabilistic graphical model; probability-based relationship; random variable; Bayes methods; Data models; Hidden Markov models; Inference algorithms; Manganese; Markov random fields; Probabilistic logic; Bayesian network; Markov network; factor graph; learning and inference; probabilisticgraphical model;
Conference_Titel :
Web Information System and Application Conference (WISA), 2013 10th
Conference_Location :
Yangzhou
Print_ISBN :
978-1-4799-3218-4
DOI :
10.1109/WISA.2013.59
