Inference and modeling of multiply sectioned Bayesian networks
Fengzhan Tian, Wang Hongwei, Lu Yuchang, Shi Chimyi · 2004
This paper first analyzes systematically two classical exact inference algorithms for local inference in multiply sectioned Bayesian networks (MSBN) and points out the factor determining the complexity of the algorithms. Furthermore, the paper proves the identity of the two algorithms, gives a unified explanation for them and finds the class of Bayesian networks in which exact inference can be performed. Finally, the paper discusses how to reduce the complexity of the global inference in MSBN and gives some basic principles to guarantee the efficiency of the whole inference.