Factorisation of Probability Trees and its Applications

dc.contributor.author	Martínez, Irene
dc.contributor.author	Moral, Serafín
dc.contributor.author	Rodríguez, Carmelo
dc.contributor.author	Salmerón Cerdán, Antonio
dc.date.accessioned	2012-05-28T09:47:45Z
dc.date.available	2012-05-28T09:47:45Z
dc.date.issued	2002
dc.identifier.citation	Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02), pp. 127-134.	es_ES
dc.identifier.uri	http://hdl.handle.net/10835/1554
dc.description.abstract	Bayesian networks can be seen as a factorisation of a joint probability distribution over a set of variables, based on the conditional independence relations amongst the variables. In this paper we show how it is possible to achieve a finer factorisation decomposing the origninal factors in which some conditions hols. The new ideas can be applied to algorithms able to deal wih factorised probabilistic potentials, as Lazy Propagation, Lazy-Penniless and Importance Sampling.	es_ES
dc.language.iso	en	es_ES
dc.source	First European Workshop on Probabilistic Graphical Models (PGM'02)	es_ES
dc.title	Factorisation of Probability Trees and its Applications	es_ES
dc.type	info:eu-repo/semantics/report	es_ES
dc.rights.accessRights	info:eu-repo/semantics/openAccess	es_ES