30 citations · 84 across the 13 of their papers we have counts for
15 papers
Improving multiple-try Metropolis with local balancing
Philippe Gagnon, Florian Maire, Giacomo Zanella
Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the appealing feature of being amenable to parallel computing. At each iteration, it samples several…
An asymptotic Peskun ordering and its application to lifted samplers
Philippe Gagnon, Florian Maire
A Peskun ordering between two samplers, implying a dominance of one over the other, is known among the Markov chain Monte Carlo community for being a remarkably strong result. It i…
On the convergence time of some non-reversible Markov chain Monte Carlo methods
Marie Vialaret, Florian Maire
It is commonly admitted that non-reversible Markov chain Monte Carlo (MCMC) algorithms usually yield more accurate MCMC estimators than their reversible counterparts. In this note,…
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling
Florian Maire, Pierre Vandekerkhove
A novel strategy that combines a given collection of -reversible Markov kernels is proposed. At each Markov transition, one of the available kernels is selected via a state-depe…
Computationally efficient inference for latent position network models
Riccardo Rastelli, Florian Maire, Nial Friel
Latent position models are widely used for the analysis of networks in a variety of research fields. In fact, these models possess a number of desirable theoretical properties, and…
Model comparison for Gibbs random fields using noisy reversible jump Markov chain Monte Carlo
Lampros Bouranis, Nial Friel, Florian Maire
The reversible jump Markov chain Monte Carlo (RJMCMC) method offers an across-model simulation approach for Bayesian estimation and model comparison, by exploring the sampling spac…
Efficient MCMC for Gibbs Random Fields using pre-computation
Aidan Boland, Nial Friel, Florian Maire
Bayesian inference of Gibbs random fields (GRFs) is often referred to as a doubly intractable problem, since the likelihood function is intractable. The exploration of the posterio…
Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets
Florian Maire, Nial Friel, Pierre Alquier
This paper introduces a framework for speeding up Bayesian inference conducted in presence of large datasets. We design a Markov chain whose transition kernel uses an (unknown) fra…
Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods
Lampros Bouranis, Nial Friel, Florian Maire
Models with intractable likelihood functions arise in areas including network analysis and spatial statistics, especially those involving Gibbs random fields. Posterior parameter e…
Online EM for Functional Data
Florian Maire, Eric Moulines, Sidonie Lefebvre
A novel approach to perform unsupervised sequential learning for functional data is proposed. Our goal is to extract reference shapes (referred to as templates) from noisy, deforme…
Adaptive Incremental Mixture Markov chain Monte Carlo
Florian Maire, Nial Friel, Antonietta Mira +1
We propose Adaptive Incremental Mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general state-space. Whi…
Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution
Lampros Bouranis, Nial Friel, Florian Maire
Exponential random graph models are an important tool in the statistical analysis of data. However, Bayesian parameter estimation for these models is extremely challenging, since e…
Light and Widely Applicable MCMC: Approximate Bayesian Inference for Large Datasets
Florian Maire, Nial Friel, Pierre Alquier
Light and Widely Applicable (LWA-) MCMC is a novel approximation of the Metropolis-Hastings kernel targeting a posterior distribution defined on a large number of observations. Ins…
On the use of Markov chain Monte Carlo methods for the sampling of mixture models
Randal Douc, Florian Maire, Jimmy Olsson
In this paper we study asymptotic properties of different data-augmentation-type Markov chain Monte Carlo algorithms sampling from mixture models comprising discrete as well as con…
Comparison of asymptotic variances of inhomogeneous Markov chains with application to Markov chain Monte Carlo methods
Florian Maire, Randal Douc, Jimmy Olsson
In this paper, we study the asymptotic variance of sample path averages for inhomogeneous Markov chains that evolve alternatingly according to two different -reversible Markov t…