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Biased Online Parameter Inference for State-Space Models

hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorDEL MORAL, Pierre
hal.structure.identifierNational University of Singapore [NUS]
dc.contributor.authorJASRA, Ajay
hal.structure.identifierBeijing Genomics Institute [Shenzhen] [BGI]
dc.contributor.authorZHOU, Yan
dc.date.accessioned2024-04-04T03:07:18Z
dc.date.available2024-04-04T03:07:18Z
dc.date.issued2017-09
dc.identifier.issn1387-5841
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193424
dc.description.abstractEnWe consider Bayesian online static parameter estimation for state-space models. This is a very important problem, but is very computationally challenging as the state-of-the art methods that are exact, often have a computational cost that grows with the time parameter; perhaps the most successful algorithm is that of SM C2 (Chopin et al., J R Stat Soc B 75: 397–426 2013). We present a version of the SM C2 algorithm which has computational cost that does not grow with the time parameter. In addition, under assumptions, the algorithm is shown to provide consistent estimates of expectations w.r.t. the posterior. However, the cost to achieve this consistency can be exponential in the dimension of the parameter space; if this exponential cost is avoided, typically the algorithm is biased. The bias is investigated from a theoretical perspective and, under assumptions, we find that the bias does not accumulate as the time parameter grows. The algorithm is implemented on several Bayesian statistical models.
dc.language.isoen
dc.publisherSpringer Verlag
dc.title.enBiased Online Parameter Inference for State-Space Models
dc.typeArticle de revue
dc.identifier.doi10.1007/s11009-016-9511-x
dc.subject.halMathématiques [math]/Probabilités [math.PR]
bordeaux.journalMethodology and Computing in Applied Probability
bordeaux.page727 - 749
bordeaux.volume19
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue3
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01669132
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01669132v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Methodology%20and%20Computing%20in%20Applied%20Probability&rft.date=2017-09&rft.volume=19&rft.issue=3&rft.spage=727%20-%20749&rft.epage=727%20-%20749&rft.eissn=1387-5841&rft.issn=1387-5841&rft.au=DEL%20MORAL,%20Pierre&JASRA,%20Ajay&ZHOU,%20Yan&rft.genre=article


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