2015
DOI: 10.1007/978-3-319-20807-7_37
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MPE Inference in Conditional Linear Gaussian Networks

Abstract: Abstract. Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is the problem of finding a configuration of the remaining variables with maximum posterior probability. This problem has previously been addressed for discrete Bayesian networks and can be solved using inference methods similar to those used for finding posterior probabilities. However, when dealing with hybrid Bayesian networks, such as conditional linear Gaussian (CLG) networks, the MPE problem has only… Show more

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Cited by 4 publications
(5 citation statements)
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“…CLGNs are a generalization of GBNs that contain both discrete (multinomial; X D ∈ X ) and continuous (Gaussian; X C ∈ X ) variables and are a special case of a hybrid Bayesian network (Salmerón et al, ). In CLGNs, discrete‐discrete, discrete‐Gaussian, and Gaussian‐Gaussian parent‐child hierarchies are permitted, but Gaussian‐discrete parent‐child hierarchies are prohibited.…”
Section: Methods and Analysesmentioning
confidence: 99%
“…CLGNs are a generalization of GBNs that contain both discrete (multinomial; X D ∈ X ) and continuous (Gaussian; X C ∈ X ) variables and are a special case of a hybrid Bayesian network (Salmerón et al, ). In CLGNs, discrete‐discrete, discrete‐Gaussian, and Gaussian‐Gaussian parent‐child hierarchies are permitted, but Gaussian‐discrete parent‐child hierarchies are prohibited.…”
Section: Methods and Analysesmentioning
confidence: 99%
“…In this case, the proposed modified framework is a hybrid Bayesian network where discrete and continuous variables coexist [121,128]. Each "Partial Risk" node is a continuous node, calculated as the product of a PHOS discrete node and a SIS continuous node.…”
Section: Hybrid Bayesian Networkmentioning
confidence: 99%
“…The set of five partial "Subsystem Risk" and the "Total Risk" continuous nodes are simple aggregation functions of probability distributions. This special type of hybrid BN is called conditional linear Gaussian network (CLGN) [123,124,128]. In the proposed framework, for the implementation of the hybrid version, the end user simply inserts the necessary values of the desired distribution directly in the equation box of each SIS node.…”
Section: Hybrid Bayesian Networkmentioning
confidence: 99%
“…(Rohmer, 2020;Ropero et al, 2018;Wisse et al, 2008). Τέλος, η (Kjaerulff and Madsen, 2008;Koller and Friedman,2009, pp189-190;Salmerón et al, 2015). Mkrtchyan et al, 2015;Wisse et al, 2008).…”
Section: κυριότερες ιδιότητες των δικτύων πεποιθήσεωνunclassified
“…Το ΔΠ διακριτού τύπου, που περιεγράφηκε στην προηγούμενη ενότητα, αφορά σε μία ημιποσοτική ανάλυση της πολλαπλής διακινδύνευσης ενός μεταλλευτικού έργου από την ΠΚΑ Koller and Friedman, 2009;Salmerón et al, 2015)…”
unclassified