90 citations · 403 across the 37 of their papers we have counts for
12 papers · 1 filter
Convergence and efficiency of adaptive importance sampling techniques with partial biasing
Gersende Fort, Benjamin Jourdain, Tony Lelièvre +1
We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which is an adaptive importance technique useful to sample multimodal target distributions.…
Self-Healing Umbrella Sampling: Convergence and efficiency
G. Fort, B. Jourdain, T. Lelievre +1
The Self-Healing Umbrella Sampling (SHUS) algorithm is an adaptive biasing algorithm which has been proposed to efficiently sample a multimodal probability measure. We show that th…
Subgeometric rates of convergence in Wasserstein distance for Markov chains
Alain Durmus, Gersende Fort, Eric Moulines
In this paper, we provide sufficient conditions for the existence of the invariant distribution and for subgeometric rates of convergence in Wasserstein distance for general state-…
Central Limit Theorems for Stochastic Approximation with controlled Markov chain dynamics
Gersende Fort
This paper provides a Central Limit Theorem (CLT) for a process satisfying a stochastic approximation (SA) equation of the form $θ_{n+1} = θ_n + γ_{n+1} H(θ_n,X_…
Quantitative convergence rates for sub-geometric Markov chains
Christophe Andrieu, Gersende Fort, Matti Vihola
We provide explicit expressions for the constants involved in the characterisation of ergodicity of sub-geometric Markov chains. The constants are determined in terms of those appe…
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…
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II
Yves F. Atchade, Gersende Fort
We prove a central limit theorem for a general class of adaptive Markov Chain Monte Carlo algorithms driven by sub-geometrically ergodic Markov kernels. We discuss in detail the sp…
State-dependent Foster-Lyapunov criteria for subgeometric convergence of Markov chains
Stephen B. Connor, Gersende Fort
We consider a form of state-dependent drift condition for a general Markov chain, whereby the chain subsampled at some deterministic time satisfies a geometric Foster-Lyapunov cond…
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels
Yves Atchadé, Gersende Fort
This paper deals with the ergodicity and the existence of a strong law of large numbers for adaptive Markov Chain Monte Carlo. We show that a diminishing adaptation assumption toge…
The ODE method for stability of skip-free Markov chains with applications to MCMC
Gersende Fort, Sean Meyn, Eric Moulines +1
Fluid limit techniques have become a central tool to analyze queueing networks over the last decade, with applications to performance analysis, simulation and optimization. In this…
Subgeometric ergodicity of strong Markov processes
G. Fort, G. O. Roberts
We derive sufficient conditions for subgeometric f-ergodicity of strongly Markovian processes. We first propose a criterion based on modulated moment of some delayed return-time to…
Practical drift conditions for subgeometric rates of convergence
Randal Douc, Gersende Fort, Eric Moulines +1
We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a ψ-irreducible aperiodic and positive recurrent transition ke…