On approximation of smoothing probabilities for hidden Markov models

2009/10/24 by Jüri Lember, Lember, J.
Computer Science · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Speech Recognition and Synthesis #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.0910.4636

openalex publication_date 2009/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 applications we consider the pointwise maximum a posteriori segmentation, and show that the corresponding risks converge.

Citations

• A tutorial on hidden Markov models and selected applications in speech recognition

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