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dc.contributor.authorBallesio, Marco
dc.contributor.authorJasra, Ajay
dc.date.accessioned2021-05-31T07:41:04Z
dc.date.available2021-05-31T07:41:04Z
dc.date.issued2021-05-24
dc.identifier.urihttp://hdl.handle.net/10754/669307
dc.description.abstractIn this paper, we consider static parameter estimation for a class of continuous-time state-space models. Our goal is to obtain an unbiased estimate of the gradient of the log-likelihood (score function), which is an estimate that is unbiased even if the stochastic processes involved in the model must be discretized in time. To achieve this goal, we apply a \emph{doubly randomized scheme} (see, e.g.,~\cite{ub_mcmc, ub_grad}), that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \cite{jacob2}. Our novel estimate helps facilitate the application of gradient-based estimation algorithms, such as stochastic-gradient Langevin descent. We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee \& Glynn estimator \cite{rhee,vihola}.
dc.description.sponsorshipThe authors were supported by KAUST baseline funding.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2105.11522.pdf
dc.rightsArchived with thanks to arXiv
dc.subjectScore Function
dc.subjectParticle Filter
dc.subjectCoupled Conditional Particle Filter
dc.titleUnbiased Estimation of the Gradient of the Log-Likelihood for a Class of Continuous-Time State-Space Models
dc.typePreprint
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.identifier.arxivid2105.11522
kaust.personBallesio, Marco
kaust.personJasra, Ajay
refterms.dateFOA2021-05-31T07:41:37Z
kaust.acknowledged.supportUnitKAUST baseline funding


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