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dc.contributor.authorCano, Andrés
dc.contributor.authorGómez Olmedo, Manuel
dc.contributor.authorMoral, Serafín
dc.contributor.authorPérez-Ariza, Cora B.
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2012-05-28T09:46:37Z
dc.date.available2012-05-28T09:46:37Z
dc.date.issued2010
dc.identifier.citationProceedings of The Fifth European Workshop on Probabilistic Graphical Models (PGM 2010), pp. 49-56.es_ES
dc.identifier.urihttp://hdl.handle.net/10835/1549
dc.description.abstractA recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computations during inference. This paper describes a learning algorithm that builds a RPT from a probability distribution. Experiments prove that this algorithm generates a good approximation of the original distribution, thus making available all the advantages provided by RPTses_ES
dc.language.isoenes_ES
dc.sourceFifth European Workshop on Probabilistic Graphical Models (PGM 2010)es_ES
dc.titleLearning recursive probability trees from probabilistic potentialses_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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