TY - JOUR
T1 - Inference in hybrid Bayesian networks
AU - Lanseth, Helge
AU - Nielsen, Thomas Dyhre
AU - Rumí, Rafael
AU - Salmerón, Antonio
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
U2 - 10.1016/j.ress.2009.02.027
DO - 10.1016/j.ress.2009.02.027
M3 - Journal article
SN - 0951-8320
VL - 94
SP - 1499
EP - 1509
JO - Reliability Engineering & System Safety
JF - Reliability Engineering & System Safety
IS - 10
ER -