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Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.spl.2013.02.005
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dc.titleLikelihood computation for hidden Markov models via generalized two-filter smoothing
dc.contributor.authorPersing, A.
dc.contributor.authorJasra, A.
dc.date.accessioned2016-06-02T10:30:17Z
dc.date.available2016-06-02T10:30:17Z
dc.date.issued2013-05
dc.identifier.citationPersing, A., Jasra, A. (2013-05). Likelihood computation for hidden Markov models via generalized two-filter smoothing. Statistics and Probability Letters 83 (5) : 1433-1442. ScholarBank@NUS Repository. https://doi.org/10.1016/j.spl.2013.02.005
dc.identifier.issn01677152
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/125056
dc.description.abstractWe introduce an estimate for the likelihood of hidden Markov models (HMMs) using sequential Monte Carlo (SMC) approximations of the generalized two-filter smoothing decomposition (Briers etal., 2010). This estimate is unbiased and a central limit theorem (CLT) is established. The new estimate is also investigated from a numerical perspective. © 2013 Elsevier B.V.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.spl.2013.02.005
dc.sourceScopus
dc.subjectGeneralized two-filter smoothing
dc.subjectLikelihood estimation
dc.subjectSequential Monte Carlo
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.spl.2013.02.005
dc.description.sourcetitleStatistics and Probability Letters
dc.description.volume83
dc.description.issue5
dc.description.page1433-1442
dc.description.codenSPLTD
dc.identifier.isiut000317808700018
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