On approximation of smoothing probabilities for hidden Markov models

Jüri Lember

Statistics & Probability Letters, 2011, vol. 81, issue 2, 310-316

Abstract: We consider the smoothing probabilities of hidden Markov model (HMM). We show that under fairly general conditions for HMM, the exponential forgetting still holds, and the smoothing probabilities can be well approximated with the ones of double-sided HMM. This makes it possible to use ergodic theorems. As an application we consider the pointwise maximum a posteriori segmentation, and show that the corresponding risks converge.

Keywords: Hidden; Markov; models; Smoothing; Segmentation (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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