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dc.contributor.authorRamos López, Darío
dc.contributor.authorMasegosa, Andrés R.
dc.contributor.authorMartínez, Ana M.
dc.contributor.authorSalmerón Cerdán, Antonio
dc.contributor.authorNielsen, Thomas D.
dc.contributor.authorLangseth, Helge
dc.contributor.authorMadsen, Anders L.
dc.date.accessioned2017-07-05T08:37:12Z
dc.date.available2017-07-05T08:37:12Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10835/4884
dc.description.abstractIn this paper, we study the maximum a posteriori (MAP) problem in dynamic hybrid Bayesian networks. We are interested in finding the sequence of values of a class variable that maximizes the posterior probability given evidence. We propose an approximate solution based on transforming the MAP problem into a simpler belief update problem. The proposed solution constructs a set of auxiliary networks by grouping consecutive instantiations of the variable of interest, thus capturing some of the potential temporal dependences between these variables while ignoring others. Belief update is carried out independently in the auxiliary models, after which the results are combined, producing a configuration of values for the class variable along the entire time sequence. Experiments have been carried out to analyze the behavior of the approach. The algorithm has been implemented using Java 8 streams, and its scalability has been evaluated.es_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourcePublicado en Progress in Artificial Intelligencees_ES
dc.subjectMAP inferencees_ES
dc.subjectHybrid Bayesian networkses_ES
dc.subjectTemporal modelses_ES
dc.titleMAP inference in dynamic hybrid Bayesian networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s13748-017-0115-7?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst#copyrightInformationes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doiDOI: 10.1007/s13748-017-0115-7


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional