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dc.contributor.authorLaw, Kody
dc.date.accessioned2017-06-08T06:32:29Z
dc.date.available2017-06-08T06:32:29Z
dc.date.issued2016-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624851
dc.description.abstractThis talk will pertain to the filtering of partially observed diffusions, with discrete-time observations. It is assumed that only biased approximations of the diffusion can be obtained, for choice of an accuracy parameter indexed by l. A multilevel estimator is proposed, consisting of a telescopic sum of increment estimators associated to the successive levels. The work associated to O( 2) mean-square error between the multilevel estimator and average with respect to the filtering distribution is shown to scale optimally, for example as O( 2) for optimal rates of convergence of the underlying diffusion approximation. The method is illustrated on some toy examples as well as estimation of interest rate based on real S&P 500 stock price data.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/78245e5ce65b463c9ee80281878c39fa1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleMultilevel particle filter
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.institutionOak Ridge National Laboratory
refterms.dateFOA2018-06-13T14:50:09Z


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