多模块贝叶斯网络中推理的简化

多模块贝叶斯网络(MSBN)引入了模块化和面向对象思想，是复杂大系统建模的有力工具．目前，如何简化MSBN中局部和全局推理的时空复杂度已成为影响其应用的关键问题．首先分析了用于局部贝叶斯网络推理的两类经典算法的时空复杂度，证明了它们本质上的一致性，并给出了统一的理论解释；进而用实验证明了影响推理复杂度的决定性因素是网络模型相应导出图的导出宽度，并指出了可以精确推理的贝叶斯网络族．最后，分析了降低MSBN全局推理复杂度的可行性，给出了简化MSBN全局推理的指导性原则．

Simplification of Inferences in Multiply Sectioned Bayesian Networks

Multiply sectioned Bayesian Networks (MSBN) support objected-oriented modeling and modularly modeling, which have become an efficient tool for modeling complex giant systems. At present, how to simplify the time and space complexity of local and global inferences in MSBN has become a key problem constraining their applications. In this paper, two classical exact inference algorithms for local inferences in MSBN are analyzed, the identity of the two algorithms is proved, and a unified explanation is given. Then it is proved that the factor determining the complexity of the inferences is the induced width of the induced graph and the class of Bayesian networks on which exact inference can be performed is found. Finally, the feasibility of reducing the complexity of global inferences in MSBN is discussed and some basic principles to simplify global inferences in MSBN are also given.