Inference in hybrid Bayesian networks

Helge Lanseth, Thomas Dyhre Nielsen, Rafael Rumí, Antonio Salmerón

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

57 Citationer (Scopus)
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Resumé

Udgivelsesdato: OCT
OriginalsprogEngelsk
TidsskriftReliability Engineering & System Safety
Vol/bind94
Udgave nummer10
Sider (fra-til)1499–1509
ISSN0951-8320
DOI
StatusUdgivet - 2009

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Bayesian networks
Large scale systems
Engines

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Lanseth, Helge ; Nielsen, Thomas Dyhre ; Rumí, Rafael ; Salmerón, Antonio. / Inference in hybrid Bayesian networks. I: Reliability Engineering & System Safety. 2009 ; Bind 94, Nr. 10. s. 1499–1509.
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abstract = "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.",
author = "Helge Lanseth and Nielsen, {Thomas Dyhre} and Rafael Rum{\'i} and Antonio Salmer{\'o}n",
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Inference in hybrid Bayesian networks. / Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael; Salmerón, Antonio.

I: Reliability Engineering & System Safety, Bind 94, Nr. 10, 2009, s. 1499–1509.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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T1 - Inference in hybrid Bayesian networks

AU - Lanseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Rumí, Rafael

AU - Salmerón, Antonio

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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.

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DO - 10.1016/j.ress.2009.02.027

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