Measures on Hidden Markov Models

Abstract

Hidden Markov models were introduced in the beginning of
the 1970's as a tool in speech recognition. During the last decade
they have been found useful in addressing problems in computational
biology such as characterising sequence families, gene finding,
structure prediction and phylogenetic analysis. In this paper
we propose several measures between hidden Markov models. We
give an efficient algorithm that computes the measures for leftright
models, e.g. profile hidden Markov models, and discuss how
to extend the algorithm to other types of models. We present an
experiment using the measures to compare hidden Markov models
for three classes of signal peptides.