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 studies the implementation of the coupling from the past (CFTP)method of Propp and Wilson (1996) in the set-up of two and three component mixtures with known components. We show that monotonicity structures can be exhibited in both cases, but that CFTP an still be costly for three...

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...

This paper proposes alternative methods for constructing estimators from accept-reject samples by incorporating the variables rejected by the algorithm.

Bayesian inference for exponential mixtures is presented in the paper, including the choice of a non-informative prior based on a location-scale reparametrization of the mixture. Adapted control sheets are proposed for studying the convergence of the associated Gibbs sampler. They exhibit a...