Likelihood computation for hidden Markov models via generalized two-filter smoothing

Adam Persing, Ajay Jasra

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We 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.
Original languageEnglish (US)
JournalStatistics and Probability Letters
Issue number5
StatePublished - May 1 2013
Externally publishedYes

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