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dc.contributor.authorBeskos, Alexandros
dc.contributor.authorCrisan, Dan
dc.contributor.authorJasra, Ajay
dc.contributor.authorKantas, Nikolas
dc.contributor.authorRuzayqat, Hamza Mahmoud
dc.date.accessioned2021-08-04T12:16:25Z
dc.date.available2020-08-24T10:08:16Z
dc.date.available2021-08-04T12:16:25Z
dc.date.issued2021-07-15
dc.identifier.citationBeskos, A., Crisan, D., Jasra, A., Kantas, N., & Ruzayqat, H. (2021). Score-Based Parameter Estimation for a Class of Continuous-Time State Space Models. SIAM Journal on Scientific Computing, 43(4), A2555–A2580. doi:10.1137/20m1362942
dc.identifier.issn1064-8275
dc.identifier.issn1095-7197
dc.identifier.doi10.1137/20m1362942
dc.identifier.urihttp://hdl.handle.net/10754/664791
dc.description.abstractWe consider the problem of parameter estimation for a class of continuous-time state space models (SSMs). In particular, we explore the case of a partially observed diffusion, with data also arriving according to a diffusion process. Based upon a standard identity of the score function, we consider two particle filter based methodologies to estimate the score function. Both methods rely on an online estimation algorithm for the score function, as described, e.g., in [P. Del Moral, A. Doucet, and S. S. Singh, M$2$AN Math. Model. Numer. Anal., 44 (2010), pp. 947--975], of $\mathcal{O}(N^2)$ cost, with $N\in\mathbb{N}$ the number of particles. The first approach employs a simple Euler discretization and standard particle smoothers and is of cost $\mathcal{O}(N^2 + N\Delta_l^{-1})$ per unit time, where $\Delta_l=2^{-l}$, $l\in\mathbb{N}_0$, is the time-discretization step. The second approach is new and based upon a novel diffusion bridge construction. It yields a new backward-type Feynman--Kac formula in continuous time for the score function and is presented along with a particle method for its approximation. Considering a time-discretization, the cost is $\mathcal{O}(N^2\Delta_l^{-1})$ per unit time. To improve computational costs, we then consider multilevel methodologies for the score function. We illustrate our parameter estimation method via stochastic gradient approaches in several numerical examples.
dc.description.sponsorshipThe work of the third and fifth authors was supported by KAUST baseline funding. The work of the first author was supported by a Leverhulme Trust Prize. The work of the second author was partially supported by EU Synergy project STUOD - DLV-856408. The work of the fourth author was supported by a JP Morgan A.I. Faculty award.
dc.publisherSociety for Industrial & Applied Mathematics (SIAM)
dc.relation.urlhttps://epubs.siam.org/doi/10.1137/20M1362942
dc.rightsArchived with thanks to SIAM Journal on Scientific Computing
dc.titleScore-Based Parameter Estimation for a Class of Continuous-Time State Space Models
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalSIAM Journal on Scientific Computing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Statistical Science, University College London, London, WC1E 6BT, UK
dc.contributor.institutionDepartment of Mathematics, Imperial College London, London, SW7 2AZ, UK
dc.identifier.volume43
dc.identifier.issue4
dc.identifier.pagesA2555-A2580
dc.identifier.arxivid2008.07803
kaust.personJasra, Ajay
kaust.personRuzayqat, Hamza Mahmoud
refterms.dateFOA2020-08-24T10:09:43Z
kaust.acknowledged.supportUnitKAUST baseline funding


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