Label switching is a well-known phenomenon that occurs in MCMC outputs targeting the parameters’ posterior distribution of many latent variable models. Although its appearence is necessary for the convergence of the simulated Markov chain, it turns out to be a problem in the estimation procedure. In a recent paper, Papastamoulis and Iliopoulos (2010) introduced the Equivalence Classes Represent...

This paper focuses on the Bayesian posterior mean estimates (or Bayes’ estimate) of the parameter set of Poisson hidden Markov models in which the observation sequence is generated by a Poisson distribution whose parameter depends on the underlining discrete-time time-homogeneous Markov chain. Although the most commonly used procedures for obtaining parameter estimates for hidden Markov models ...

The Markov Chain Monte Carlo method is at the heart of most fully-polynomial randomized approximation schemes for #P-complete problems such as estimating the permanent or the value of a polytope. It is therefore very natural and important to determine whether quantum computers can speed-up classical mixing processes based on Markov chains. To this end, we present a new quantum algorithm, making...

A particular Markov chain Monte Carlo algorithmis constructed to allow Bayesian inference in a hidden Markov model used in hematology. The algorithm has an outer Gibbsian structure, and incorporates both Metropolis and Hastings updates to move through the space of possible hidden states. While somewhat sophisticated, this algorithm still has problems getting around the infinite-dimensional spac...

In this paper, we study almost sure convergence of a dynamic average consensus algorithm which allows distributed computation of the product of n time-varying conditional probability density functions. These density functions (often called as “belief functions”) correspond to the conditional probability of observations given the state of an underlying Markov chain, which is observed by n differ...