On approximation of smoothing probabilities for hidden Markov models

Details On approximation of smoothing probabilities for hidden Markov models On approximation of smoothing probabilities for hidden Markov models

2009-10-24

arxiv.org/abs/0910.4636v3

Statistics

Machine Learning

submitted to Statistics and Probability Letters

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

On approximation of smoothing probabilities for hidden Markov models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On approximation of smoothing probabilities for hidden Markov models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On approximation of smoothing probabilities for hidden Markov models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-211666