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dc.contributor.authorSalmeron, Antonio
dc.contributor.authorRumi, Rafael
dc.contributor.authorLangseth, Helge
dc.contributor.authorNielsen, Thomas D.
dc.contributor.authorMadsen, Anders L.
dc.date.accessioned2018-09-06T08:11:28Z
dc.date.available2018-09-06T08:11:28Z
dc.date.created2018-08-31T09:17:01Z
dc.date.issued2018
dc.identifier.citationThe journal of artificial intelligence research. 2018, 63 799-828.nb_NO
dc.identifier.issn1076-9757
dc.identifier.urihttp://hdl.handle.net/11250/2561116
dc.description.abstractHybrid Bayesian networks have received an increasing attention during the last years. The difference with respect to standard Bayesian networks is that they can host discrete and continuous variables simultaneously, which extends the applicability of the Bayesian network framework in general. However, this extra feature also comes at a cost: inference in these types of models is computationally more challenging and the underlying models and updating procedures may not even support closed-form solutions. In this paper we provide an overview of the main trends and principled approaches for performing inference in hybrid Bayesian networks. The methods covered in the paper are organized and discussed according to their methodological basis. We consider how the methods have been extended and adapted to also include (hybrid) dynamic Bayesian networks, and we end with an overview of established software systems supporting inference in these types of models.nb_NO
dc.language.isoengnb_NO
dc.publisherAI Access Foundationnb_NO
dc.relation.urihttps://www.jair.org/index.php/jair/article/view/11228/26433
dc.titleA Review of Inference Algorithms for Hybrid Bayesian Networksnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber799-828nb_NO
dc.source.volume63nb_NO
dc.source.journalThe journal of artificial intelligence researchnb_NO
dc.identifier.doi10.1613/jair.1.11228
dc.identifier.cristin1605699
dc.relation.projectEC/FP7/619209nb_NO
dc.description.localcode(c) 2018 AI Access Foundation. All rights reserved.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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