Aalborg Universitet Inference in hybrid Bayesian networks
Aalborg Universitet Inference in hybrid Bayesian networks
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发表时间：
2008
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通讯作者：
H. Langseth;Thomas D. Nielsen;R. Rumí;A. Salmerón
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作者：
H. Langseth;Thomas D. Nielsen;R. Rumí;A. Salmerón
自20世纪80年代以来，贝叶斯网络(BNS)在建立复杂系统的统计模型方面变得越来越流行。对于布尔系统尤其如此，在布尔系统中，BNS往往被证明是比传统可靠性技术(如故障树和可靠性框图)更有效的建模框架。然而，BNS计算引擎的局限性阻碍了BNS在同时包含离散变量和连续变量的域(所谓的混合域)中变得同样受欢迎。本文针对这些困难，总结了近十年来混合贝叶斯网络中推理的一些研究成果。这些讨论与估计人的可靠性的一个示例模型相关联。
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees and reliability block diagrams). However, limitations in the BNs’ calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade’s research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.