Show simple item record

dc.contributor.authorMoral, Serafín
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2017-07-07T07:17:11Z
dc.date.available2017-07-07T07:17:11Z
dc.date.issued2005
dc.identifier.urihttp://hdl.handle.net/10835/4893
dc.description.abstractIn this paper we introduce a new dynamic importance sampling propagation algorithm for Bayesian networks. Importance sampling is based on using an auxiliary sampling distribution from which a set of con gurations of the variables in the network is drawn, and the performance of the algorithm depends on the variance of the weights associated with the simulated con gurations. The basic idea of dynamic importance sampling is to use the simulation of a con guration to modify the sampling distribution in order to improve its quality and so reducing the variance of the future weights. The paper shows that this can be achieved with a low computational effort. The experiments carried out show that the nal results can be very good even in the case that the initial sampling distribution is far away from the optimum.es_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBayesian networkses_ES
dc.subjectProbability propagationes_ES
dc.subjectApproximate algorithmses_ES
dc.subjectImportance samplinges_ES
dc.subjectProbability treeses_ES
dc.titleDynamic Importance Sampling in Bayesian Networks Based on Probability Treeses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doihttps://doi.org/10.1016/j.ijar.2004.05.005


Files in this item

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional