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dc.contributor.authorMoral, Serafín
dc.contributor.authorRumí, Rafael
dc.contributor.authorSalmerón Cerdán, Antonio 
dc.date.accessioned2012-05-28T09:50:38Z
dc.date.available2012-05-28T09:50:38Z
dc.date.issued2002
dc.identifier.citationProceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02), pp. 135-143.es_ES
dc.identifier.urihttp://hdl.handle.net/10835/1557
dc.description.abstractThe MTE (mixture of truncated exponentials) model allows to deal with Bayesian networks containing discrete and continuous variables simultaneously. One of the features of this model is that standard propagation algorithms can be applied. In this paper, we study the problem of estimating these models from data. We propose an iterative algorithm based on least squares approximation. The performance of the algorithm is tested both with artificial and actual data.es_ES
dc.language.isoenes_ES
dc.sourceFirst European Workshop on Probabilistic Graphical Models (PGM'02)es_ES
dc.titleEstimating mixtures of truncated exponentials from dataes_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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