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dc.contributor.authorJasra, Ajay
dc.contributor.authorLaw, Kody
dc.contributor.authorYu, Fangyuan
dc.date.accessioned2020-02-27T05:50:27Z
dc.date.available2020-02-27T05:50:27Z
dc.date.issued2020-02-10
dc.identifier.urihttp://hdl.handle.net/10754/661746
dc.description.abstractIn this article we consider a Monte Carlo-based method to filter partially observed diffusions observed at regular and discrete times. Given access only to Euler discretizations of the diffusion process, we present a new procedure which can return online estimates of the filtering distribution with no discretization bias and finite variance. Our approach is based upon a novel double application of the randomization methods of Rhee & Glynn (2015) along with the multilevel particle filter (MLPF) approach of Jasra et al (2017). A numerical comparison of our new approach with the MLPF, on a single processor, shows that similar errors are possible for a mild increase in computational cost. However, the new method scales strongly to arbitrarily many processors.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2002.03747
dc.rightsArchived with thanks to arXiv
dc.titleUnbiased Filtering of a Class of Partially Observed Diffusions
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.eprint.versionPre-print
dc.contributor.institutionSchool of Mathematics, University of Manchester, Manchester, M13 9PL, UK
dc.identifier.arxivid2002.03747
kaust.personJasra, Ajay
kaust.personYu, Fangyuan
refterms.dateFOA2020-02-27T05:53:04Z


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