Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II (2009)
Atchade, Yves F., Fort, Gersende
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 special case of...
Estimation of cosmological parameters using adaptive importance sampling (2009)
Wraith, Darren, Kilbinger, Martin, Benabed, Karim, Cappé, Olivier, Cardoso, Jean-François, Fort, Gersende, ...
We present a Bayesian sampling algorithm called adaptive importance sampling or Population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to...
State-dependent Foster-Lyapunov criteria for subgeometric convergence of Markov chains (2009)
Connor, Stephen B., Fort, Gersende
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 condition. We present...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels (2008)
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 together with a drift...
Forgetting of the initial distribution for Hidden Markov Models (2008)
Douc, Randal, Fort, Gersende, Moulines, Eric, Priouret, Pierre
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 filter, at a...
Forgetting of the initial distribution for Hidden Markov Models (2008)
Douc, Randal, Fort, Gersende, Moulines, Eric, Priouret, Pierre
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 filter, at a...
The ODE method for stability of skip-free Markov chains with applications to MCMC (2008)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper, some of...
The ODE method for stability of skip-free Markov chains with applications to MCMC (2008)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper, some of...
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels (2008)
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 together with a drift...
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels (2008)
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 together with a drift...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels (2008)
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 together with a drift...
Limit theorems for some adaptive MCMC algorithms with subgeometric kernels (2008)
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 together with a drift...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
On adaptive stratification (2008)
Etoré, Pierre, Fort, Gersende, Jourdain, Benjamin, Moulines, Eric
This paper investigates the use of stratified sampling as a variance reduction technique for approximating integrals over large dimensional spaces. The accuracy of this method critically depends on...
Practical drift conditions for subgeometric rates of convergence (2007)
Douc, Randal, Fort, Gersende, Moulines, Eric, Soulier, Philippe
We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a \psi-irreducible aperiodic and positive recurrent transition kernel. This...
Forgetting of the initial distribution for Hidden Markov Models (2007)
Douc, Randal, Fort, Gersende, Moulines, Eric, Priouret, Pierre
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 filter, at a...
ODE methods for skip-free Markov chain stability with applications to MCMC (2006)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper some of...
The ODE method for stability of skip-free Markov chains with applications to MCMC (2006)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper, some of...
ODE methods for skip-free Markov chain stability with applications to MCMC (2006)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper some of...
ODE methods for skip-free Markov chain stability with applications to MCMC (2006)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper some of...
ODE methods for skip-free Markov chain stability with applications to MCMC (2006)
Fort, Gersende, Meyn, Sean, Moulines, Eric, Priouret, Pierre
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 paper some of...
Subgeometric rates of convergence of f-ergodic strong Markov processes (2006)
Douc, Randal, Fort, Gersende, Guillin, Arnaud
We provide a condition for f-ergodicity of strong Markov processes at a subgeometric rate. This condition is couched in terms of a supermartingale property for a functional of the Markov process....
Subgeometric rates of convergence of f-ergodic strong Markov processes (2006)
Douc, Randal, Fort, Gersende, Guillin, Arnaud
We provide a condition for f-ergodicity of strong Markov processes at a subgeometric rate. This condition is couched in terms of a supermartingale property for a functional of the Markov process....
Subgeometric rates of convergence of f-ergodic strong Markov processes (2006)
Douc, Randal, Fort, Gersende, Guillin, Arnaud
We provide a condition for f-ergodicity of strong Markov processes at a subgeometric rate. This condition is couched in terms of a supermartingale property for a functional of the Markov process....
Subgeometric rates of convergence of f-ergodic strong Markov processes (2006)
Douc, Randal, Fort, Gersende, Guillin, Arnaud
We provide a condition for f-ergodicity of strong Markov processes at a subgeometric rate. This condition is couched in terms of a supermartingale property for a functional of the Markov process....
Subgeometric rates of convergence of f-ergodic strong Markov processes (2006)
Douc, Randal, Fort, Gersende, Guillin, Arnaud
We provide a condition for f-ergodicity of strong Markov processes at a subgeometric rate. This condition is couched in terms of a supermartingale property for a functional of the Markov process....
A convergence theorem for variational EM-like algorithms: application to image segmentation (2005)
Forbes, Florence, Fort, Gersende
Variational Expectation Maximization (VEM) algorithms refer to a class of procedures resulting from the introduction in standard Expectation Maximization (EM) algorithms of variational approximation...
A convergence theorem for variational EM-like algorithms: application to image segmentation (2005)
Forbes, Florence, Fort, Gersende
Variational Expectation Maximization (VEM) algorithms refer to a class of procedures resulting from the introduction in standard Expectation Maximization (EM) algorithms of variational approximation...
A convergence theorem for variational EM-like algorithms: application to image segmentation (2005)
Forbes, Florence, Fort, Gersende
Variational Expectation Maximization (VEM) algorithms refer to a class of procedures resulting from the introduction in standard Expectation Maximization (EM) algorithms of variational approximation...
A convergence theorem for variational EM-like algorithms: application to image segmentation (2005)
Forbes, Florence, Fort, Gersende
Variational Expectation Maximization (VEM) algorithms refer to a class of procedures resulting from the introduction in standard Expectation Maximization (EM) algorithms of variational approximation...
Classification using partial least squares with penalized logistic regression (2005)
Fort, Gersende, Lambert-Lacroix, Sophie
Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to...
Practical drift conditions for subgeometric rates of convergence (2004)
Douc, Randal, Fort, Gersende, Moulines, Eric, Soulier, Philippe
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 kernel. This...
Practical drift conditions for subgeometric rates of convergence (2004)
Douc, Randal, Fort, Gersende, Moulines, Eric, Soulier, Philippe
We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a \psi-irreducible aperiodic and positive recurrent transition kernel. This...
BIOINFORMATICS ORIGINAL PAPER (2004)
Gene Expression, Gersende Fort, Sophie Lambert-lacroix
Classification using partial least squares with penalized logistic regression
Randal Douc, Gersende Fort, Eric Moulines
We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a-irreducible aperiodic and positive recurrent transition kernel. This condition,...
Practical Drift Conditions for Subgeometric Rates of Convergence (2004)
Randal Douc Gersende, Gersende Fort, Eric Moulines
We present a new drift condition which implies rates of convergence to the stationary distribution of the iterates of a -irreducible aperiodic and positive recurrent transition kernel. This...
Classification using Partial Least Squares with penalized logistic regression (2004)
Fort, Gersende, Lambert-Lacroix, Sophie
Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to...
Classification using Partial Least Squares with penalized logistic regression (2004)
Fort, Gersende, Lambert-Lacroix, Sophie
Motivation: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to...
Convergence of the Monte Carlo expectation maximization for curved exponential families (2003)
Fort, Gersende, Moulines, Eric
The Monte Carlo expectation maximization (MCEM) algorithm is a versatile tool for inference in incomplete data models, especially when used in combination with Markov chain Monte Carlo simulation...
2003), Computable bounds for V-geometric ergodicity of Markov transition kernels (2002)
This paper discusses quantitative bounds for the uctuations of the n-step transition law of a Markov kernel P on a general state space kP ; )k. The uctuations are successively measured in total...
Convergence Of The Monte Carlo EM For Curved Exponential Families (2000)
Gersende Fort, Gersende Fort, Eric Moulines, Eric Moulines
The Monte Carlo Expectation Maximization (MCEM) algorithm (Wei and Tanner (1991)), a stochastic...
Convergence of the Monte Carlo EM for Curved Exponential Families (2000)
The Monte-Carlo Expectation Maximization (MCEM) algorithm is a versatile tool for inference in incomplete data models...
V-Subgeometric ergodicity for a Hastings-Metropolis algorithm
Fort, Gersende, Moulines, Eric
We study the symmetric random-walk Hastings-Metropolis algorithm in situations where the density is not log-concave in the tails. We show that, under mild technical conditions this algorithm is...