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dc.contributor.authorPérez-Bernabé, Inmaculada
dc.contributor.authorSalmerón Cerdán, Antonio
dc.contributor.authorLangseth, Helge
dc.date.accessioned2017-06-14T10:01:40Z
dc.date.available2017-06-14T10:01:40Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10835/4859
dc.description.abstractMixtures of Truncated Basis Functions (MoTBFs) have recently been proposed for modelling univariate and joint distributions in hybrid Bayesian networks. In this paper we analyse the problem of learning conditional MoTBF distributions from data. Our approach utilizes a new technique for learning joint MoTBF densities, then propose a method for using these to generate the conditional distributions. The main contribution of this work is conveyed through an empirical investigation into the properties of the new learning procedure, where we also compare the merits of our approach to those obtained by other proposals.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.titleLearning Conditional Distributions using Mixtures of Truncated Basis Functionses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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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