On approximation of smoothing probabilities for hidden Markov models
Machine Learning
2011-05-11 v3 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.0910.4636,
title = {On approximation of smoothing probabilities for hidden Markov models},
author = {J. Lember},
journal= {arXiv preprint arXiv:0910.4636},
year = {2011}
}
Comments
submitted to Statistics and Probability Letters