The focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an important role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov m...

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances. 1. Introduction. The unifying perspect...

Dirichlet process (DP) provides a nonparametric prior for the mixture model that allows for the automatic detection of the number of hidden states. Recent introduction of variational Bayesian (VB) inference as a deterministic approach makes it practical to large-scale realworld problems. However, the models proposed so far have intrinsic limitations when used on noisy datasets and in situations...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

A hidden Markov model is considered where the dynamics of the hidden process change at a random ‘change point’ . In principle this gives rise to a non-linear filter but closed form recursive estimates are obtained for the conditional distribution of the hidden process and of .