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dc.contributor.authorJasra, Ajay
dc.date.accessioned2017-06-08T06:32:29Z
dc.date.available2017-06-08T06:32:29Z
dc.date.issued2016-01-05
dc.identifier.urihttp://hdl.handle.net/10754/624843
dc.description.abstractMultilevel Monte-Carlo methods provide a powerful computational technique for reducing the computational cost of estimating expectations for a given computational effort. They are particularly relevant for computational problems when approximate distributions are determined via a resolution parameter h, with h=0 giving the theoretical exact distribution (e.g. SDEs or inverse problems with PDEs). The method provides a benefit by coupling samples from successive resolutions, and estimating differences of successive expectations. We develop a methodology that brings Sequential Monte-Carlo (SMC) algorithms within the framework of the Multilevel idea, as SMC provides a natural set-up for coupling samples over different resolutions. We prove that the new algorithm indeed preserves the benefits of the multilevel principle, even if samples at all resolutions are now correlated.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/c9363bfb7c9c4c11ae0d82fe3a7aeb1d1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleMultilevel sequential Monte-Carlo samplers
dc.typePresentation
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
dc.contributor.institutionNational University of Singapore
refterms.dateFOA2018-06-13T14:49:28Z


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