(First draft March 2005; revised November 2005) Abstract This paper extends some adaptive schemes that have been developed for the Random Walk Metropolis algorithm to more general versions of the… (More)

We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an "optimal" target process via a learning procedure. We show, under appropriate conditions, that the… (More)

A large number of statistical models are “doubly-intractable”: the likelihood normalising term, which is a function of the model parameters, is intractable, as well as the marginal likelihood (model… (More)

In general, the transition probability P of the Markov chain depends on some tuning parameter θ defined on some space Θ which can be either finite dimensional or infinite dimensional. The success of… (More)

A general scheme to exploit Exact-Approximate MCMC methodology for intractable likelihoods is suggested. By representing the intractable likelihood as an infinite Maclaurin or Geometric series… (More)

Abstract Under a compactness assumption, we show that a φ-irreducible and aperiodic MetropolisHastings chain is geometrically ergodic if and only if its rejection probability is bounded away from… (More)

We consider the problem of computing a positive definite p × p inverse covariance matrix aka precision matrix θ = (θij) which optimizes a regularized Gaussian maximum likelihood problem, with the… (More)

This paper deals with the Bayesian estimation of high dimensional Gaussian graphical models. We develop a quasi-Bayesian implementation of the neighborhood selection method of Meinshausen and… (More)

We consider the Bayesian analysis of a high-dimensional statistical model with a spike-and-slab prior, and we study the forwardbackward envelope of the posterior distribution – that we denotes Π̌γ… (More)

This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network… (More)