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...

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...

A stationary Gaussian process is said to be long-range dependent (resp. anti-persistent)if its spectral density f() can be written as f() = ()-2dg(()), where 0 d 1/2(resp. -1/2 d 0), and g is continuous. We propose a novel Bayesian nonparametricapproach for the estimation of the spectral...

We introduce a new class of Sequential Monte Carlo (SMC) methods, whichwe call free energy SMC. This class is inspired by free energy methods, whichoriginate from Physics, and where one samples from a biased distribution suchthat a given function !(") of the state " is forced to be uniformly...