Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.spl.2013.02.005
Title: Likelihood computation for hidden Markov models via generalized two-filter smoothing
Authors: Persing, A.
Jasra, A. 
Keywords: Generalized two-filter smoothing
Likelihood estimation
Sequential Monte Carlo
Issue Date: May-2013
Citation: Persing, 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
Abstract: 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.
Source Title: Statistics and Probability Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/125056
ISSN: 01677152
DOI: 10.1016/j.spl.2013.02.005
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