Exact Computation of Kullback-Leibler Distance for Hidden Markov Trees and Models
Information Theory
2012-10-10 v2 math.IT
Abstract
We suggest new recursive formulas to compute the exact value of the Kullback-Leibler distance (KLD) between two general Hidden Markov Trees (HMTs). For homogeneous HMTs with regular topology, such as homogeneous Hidden Markov Models (HMMs), we obtain a closed-form expression for the KLD when no evidence is given. We generalize our recursive formulas to the case of HMMs conditioned on the observable variables. Our proposed formulas are validated through several numerical examples in which we compare the exact KLD value with Monte Carlo estimations.
Keywords
Cite
@article{arxiv.1112.3257,
title = {Exact Computation of Kullback-Leibler Distance for Hidden Markov Trees and Models},
author = {Vittorio Perduca and Grégory Nuel},
journal= {arXiv preprint arXiv:1112.3257},
year = {2012}
}
Comments
The present work is currently undergoing a major revision; a new version will be soon updated. Please do not use the present version