90 citations · 403 across the 37 of their papers we have counts for
4 papers · 1 filter
Adaptive MCMC with online relabeling
Rémi Bardenet, Olivier Cappé, Gersende Fort +1
When targeting a distribution that is artificially invariant under some permutations, Markov chain Monte Carlo (MCMC) algorithms face the label-switching problem, rendering margina…
Convergence of the Wang-Landau algorithm
Gersende Fort, Benjamin Jourdain, Estelle Kuhn +2
We analyze the convergence properties of the Wang-Landau algorithm. This sampling method belongs to the general class of adaptive importance sampling strategies which use the free…
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…
Performance of a Distributed Stochastic Approximation Algorithm
Pascal Bianchi, Gersende Fort, Walid Hachem
In this paper, a distributed stochastic approximation algorithm is studied. Applications of such algorithms include decentralized estimation, optimization, control or computing. Th…