MCMC control spreadsheets for exponential mixture estimation

This article presents Bayesian inference for exponential mixtures, includin g the choice of a noninformative prior based on a location-scale reparamete rization of the mixture. Adapted control sheets are proposed for studying t he convergence of the associated Gibbs sampler. They exhibit a strong lock of stability in the allocations of the observations to the different compon ents of the mixture, The setup is extended to the case when the number of c omponents in the mixture is unknown and a reversible jump MCMC technique is implemented. The results are illustrated on simulations and a real dataset .