488 citations · 3k across the 119 of their papers we have counts for
15 papers · 1 filter
State and parameter learning with PaRIS particle Gibbs
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff +2
Non-linear state-space models, also known as general hidden Markov models, are ubiquitous in statistical machine learning, being the most classical generative models for serial dat…
Particle-based, rapid incremental smoother meets particle Gibbs
Gabriel Cardoso, Eric Moulines, Jimmy Olsson
The particle-based, rapid incremental smoother (PARIS) is a sequential Monte Carlo technique allowing for efficient online approximation of expectations of additive functionals und…
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier, Maxime Vono, Alain Durmus +1
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Marko…
f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models
Belhal Karimi, Marc Lavielle, Eric Moulines
The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely us…
On stability of a class of filters for non-linear stochastic systems
Toni Karvonen, Silvère Bonnabel, Eric Moulines +1
This article develops a comprehensive framework for stability analysis of a broad class of commonly used continuous and discrete time-filters for stochastic dynamic systems with no…
Diffusion approximations and control variates for MCMC
Nicolas Brosse, Alain Durmus, Sean Meyn +2
A new methodology is presented for the construction of control variates to reduce the variance of additive functionals of Markov Chain Monte Carlo (MCMC) samplers. Our control vari…