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

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 et al., 2010). This estimate is unbiased and a central limit theorem (CLT) is established. The new estimate is also investigated from a numerical perspective.

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Year of publication:	2013
Authors:	Persing, Adam ; Jasra, Ajay
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 5, p. 1433-1442
Publisher:	Elsevier
Subject:	Likelihood estimation \| Sequential Monte Carlo \| Generalized two-filter smoothing

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011040015