Hidden Markov models is an extension of mixture models providing a flexible class of models exhibiting dependence and a possibly large degree of variability. In this paper the authors show how jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number...

In complex models like hidden Markov chains, the convergence of the MCMC algorithms used to approximate the posterior distribution and the Bayes estimates of the parameters of interest must be controlled in a robust manner. We propose in this paper a series of on-line controls, which rely on...

This paper synthesises a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model for the cases of normal and Poisson underlying distribution.

We propose a perfect sampler for mixtures of distributions, in the spirit of Mira and Roberts (1999), building on Hobert, Robert and Titterington (199). The moethod relies on a marginalisation akin to Rao-Blackwellisation which illustrates the Duality Principle of Diebolt and Robert (1994) and...

A simulation method based on importance sampling, Gibbs and Metropolis-Hastings techniques allows to approximate the ratio between the likelihhod function computed for two different parameter values. Thus it is possible to approximate the maximum likelihood estimator in the general framework of...