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dc.contributor.authorCampos Ibáñez, Luis Miguel 
dc.date.accessioned2014-07-18T08:37:18Z
dc.date.available2014-07-18T08:37:18Z
dc.date.issued2006
dc.identifier.citationCampos, L.M. A scoring function for learning Bayesian networks based on mutual information and conditional independence tests. Journal of Machine Learning Research, 7: 2149-2187 (2006). [http://hdl.handle.net/10481/32709]es_ES
dc.identifier.issn1532-4435
dc.identifier.urihttp://hdl.handle.net/10481/32709
dc.description.abstractWe propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.es_ES
dc.description.sponsorshipI would like to acknowledge support for this work from the Spanish ‘Consejería de Innovación Ciencia y Empresa de la Junta de Andalucía’, under Project TIC-276.es_ES
dc.language.isoenges_ES
dc.publisherMIT Presses_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es_ES
dc.subjectBayesian networkses_ES
dc.subjectScoring functionses_ES
dc.subjectLearninges_ES
dc.subjectMutual informationes_ES
dc.subjectConditional independence testses_ES
dc.titleA scoring function for learning Bayesian networks based on mutual information and conditional independence testses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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