title: Inference in multiply sectioned Bayesian networks: methods and performance comparison.
abstract: This paper extends lazy propagation for inference in single-agent Bayesian networks (BNs) to multiagent lazy inference in multiply sectioned BNs (MSBNs). Two methods are proposed using distinct runtime structures. It was proved that the new methods are exact and efficient when the domain structure is sparse. Both improve space and time complexity more than the existing method, which allows multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. The relative performances of the three methods are compared analytically and experimentally.
authors.affiliation: University of Guelph, Guelph, ON N1G 2W1, Canada. yxiang@cis.uoguelph.ca
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiang
authors.forename: Finn V
authors.initials: FV
authors.lastname: Jensen
authors.forename: Xiaoyun
authors.initials: X
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003657:Decision Making; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*