Probabilistic graphical models in artificial intelligence

被引：54

作者：

Larranaga, P. ^{[2
]}

Moral, S. ^{[1
]}

机构：

[1] Univ Granada, Comp Sci & Artificial Intelligence Dept, Granada, Spain

[2] Tech Univ Madrid, Dept Artificial Intelligence, Madrid, Spain

来源：

APPLIED SOFT COMPUTING | 2011年 / 11卷 / 02期

关键词：

Probability; Uncertain reasoning; Bayesian networks; Gaussian networks; Credal networks; Factor graphs; Kikuchi approximations; Decision-making; Classification; Optimization; Metaheuristics; Genomic; Forensic; LEARNING BAYESIAN NETWORKS; MONTE-CARLO METHOD; INFLUENCE DIAGRAMS; EXPERT-SYSTEMS; EM ALGORITHM; DISTRIBUTIONS; COMPLEXITY; LIKELIHOOD; INFERENCE;

D O I：

10.1016/j.asoc.2008.01.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we review the role of probabilistic graphical models in artificial intelligence. We start by giving an account of the early years when there was important controversy about the suitability of probability for intelligent systems. We then discuss the main milestones for the foundations of graphical models starting with Pearl's pioneering work. Some of the main techniques for problem solving (abduction, classification, and decision-making) are briefly explained. Finally, we propose some important challenges for future research and highlight relevant applications (forensic reasoning, genomics and the use of graphical models as a general optimization tool). (C) 2008 Elsevier B. V. All rights reserved.

引用

页码：1511 / 1528

页数：18

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