95 citations · 168 across the 5 of their papers we have counts for
6 papers
Convergence of adaptive and interacting Markov chain Monte Carlo algorithms
G. Fort, E. Moulines, P. Priouret
Adaptive and interacting Markov chain Monte Carlo algorithms (MCMC) have been recently introduced in the literature. These novel simulation algorithms are designed to increase the…
A simple variance inequality for U-statistics of a Markov chain with applications
Gersende Fort, Eric Moulines, Pierre Priouret +1
We establish a simple variance inequality for U-statistics whose underlying sequence of random variables is an ergodic Markov Chain. The constants in this inequality are explicit a…
A central limit theorem for adaptive and interacting Markov chains
Gersende Fort, Eric Moulines, Pierre Priouret +1
Adaptive and interacting Markov Chains Monte Carlo (MCMC) algorithms are a novel class of non-Markovian algorithms aimed at improving the simulation efficiency for complicated targ…
Forgetting of the initial distribution for Hidden Markov Models
Randal Douc, Gersende Fort, Eric Moulines +1
The forgetting of the initial distribution for discrete Hidden Markov Models (HMM) is addressed: a new set of conditions is proposed, to establish the forgetting property of the fi…
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…
On recursive estimation for time varying autoregressive processes
Eric Moulines, Pierre Priouret, François Roueff
This paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are prov…