International audienceOne challenging task in MCMC methods is the choice of the proposal density. It should ideally provide an accurate approximation of the target density with a low computational cost. In this paper, we are interested in Langevin diffusion where the proposal accounts for a directional component. We propose a novel method for tuning the related drift term. This term is preconditioned by an adaptive matrix based on a Majorize-Minimize strategy. This new procedure is shown to exhibit a good performance in a multispectral image restoration example
Global fits of physics models require efficient methods for exploring high-dimensional and/or multim...
International audiencePopulation Monte Carlo (PMC) algorithms are a family of adaptive importance sa...
International audienceThis paper introduces a new Markov Chain Monte Carlo method for Bayesian varia...
International audienceOne challenging task in MCMC methods is the choice of the proposal density. It...
International audienceThe dimension and the complexity of inference problems have dramatically incre...
International audienceIn this paper, we derive a novel MH proposal, inspired from Langevin dynamics,...
This paper proposes accelerated subspace optimization methods in the context of image restoration. S...
Bayesian approaches are widely used in signal processing applications. In order to derive plausible...
Markov Chain Monte Carlo methods are widely used in signal processing and communications for statist...
International audienceMarkov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to e...
We introduce new Gaussian proposals to improve the efficiency of the standard Hastings–Metropolis al...
Global fits of physics models require efficient methods for exploring high-dimensional and/or multim...
International audiencePopulation Monte Carlo (PMC) algorithms are a family of adaptive importance sa...
International audienceThis paper introduces a new Markov Chain Monte Carlo method for Bayesian varia...
International audienceOne challenging task in MCMC methods is the choice of the proposal density. It...
International audienceThe dimension and the complexity of inference problems have dramatically incre...
International audienceIn this paper, we derive a novel MH proposal, inspired from Langevin dynamics,...
This paper proposes accelerated subspace optimization methods in the context of image restoration. S...
Bayesian approaches are widely used in signal processing applications. In order to derive plausible...
Markov Chain Monte Carlo methods are widely used in signal processing and communications for statist...
International audienceMarkov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to e...
We introduce new Gaussian proposals to improve the efficiency of the standard Hastings–Metropolis al...
Global fits of physics models require efficient methods for exploring high-dimensional and/or multim...
International audiencePopulation Monte Carlo (PMC) algorithms are a family of adaptive importance sa...
International audienceThis paper introduces a new Markov Chain Monte Carlo method for Bayesian varia...