The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm which operates on the extended space of the auxiliary variables generated by an interacting particle system. In particular, it samples the discrete variables that determine the particle genealogy. We propose a coupling...

This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply from the authors.

A Monte Carlo algorithm is said to be adaptive if it can adjust automaticallyits current proposal distribution, using past simulations. The choice of the para-metric family that defines the set of proposal distributions is critical for a goodperformance. We treat the problem of constructing such...

This document is the aggregation of several discussions of Lopes et al. (2010) we submitted tothe proceedings of the Ninth Valencia Meeting, held in Benidorm, Spain, on June 3–8, 2010, inconjunction with Hedibert Lopes’ talk at this meeting. The main point in those discussions is...

Because of their multimodality, mixture posterior densities are difficult to sample withstandard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhancethe sampling of MCMC in this context, using a biasing procedure which originates fromcomputational statistical physics. The...