90 citations · 403 across the 37 of their papers we have counts for
11 papers · 1 filter
Convergence of Markovian Stochastic Approximation with discontinuous dynamics
Gersende Fort, Eric Moulines, Amandine Schreck +1
This paper is devoted to the convergence analysis of stochastic approximation algorithms of the form where is…
On perturbed proximal gradient algorithms
Yves F. Atchade, Gersende Fort, Eric Moulines
We study a version of the proximal gradient algorithm for which the gradient is intractable and is approximated by Monte Carlo methods (and in particular Markov Chain Monte Carlo).…
A shrinkage-thresholding Metropolis adjusted Langevin algorithm for Bayesian variable selection
Amandine Schreck, Gersende Fort, Sylvain Le Corff +1
This paper introduces a new Markov Chain Monte Carlo method for Bayesian variable selection in high dimensional settings. The algorithm is a Hastings-Metropolis sampler with a prop…
Adaptive Equi-Energy Sampler : Convergence and Illustration
Amandine Schreck, Gersende Fort, Eric Moulines
Markov chain Monte Carlo (MCMC) methods allow to sample a distribution known up to a multiplicative constant. Classical MCMC samplers are known to have very poor mixing properties…
Convergence of a Particle-based Approximation of the Block Online Expectation Maximization Algorithm
Sylvain Le Corff, Gersende Fort
Online variants of the Expectation Maximization (EM) algorithm have recently been proposed to perform parameter inference with large data sets or data streams, in independent laten…
Supplement paper to "Online Expectation Maximization based algorithms for inference in hidden Markov models"
Sylvain Le Corff, Gersende Fort
This is a supplementary material to the paper "Online Expectation Maximization based algorithms for inference in hidden Markov models". It contains further technical derivations an…