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dc.contributor.authorSalmerón Cerdán, Antonio
dc.contributor.authorRamos López, Darío
dc.contributor.authorBorchani, Hanen
dc.contributor.authorMartínez, Ana M.
dc.contributor.authorMasegosa, Andrés R.
dc.contributor.authorFernández, Antonio
dc.contributor.authorLangseth, Helge
dc.contributor.authorMadsen, Anders L.
dc.contributor.authorNielsen, Thomas D.
dc.date.accessioned2017-06-14T09:57:01Z
dc.date.available2017-06-14T09:57:01Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10835/4858
dc.description.abstractIn this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic networks show how a parallel version importance sampling, and more precisely evidence weighting, is a promising scheme, as it is accurate and scales up with respect to available computing resources.es_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleParallel Importance Sampling in Conditional Linear Gaussian Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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