30 citations · 84 across the 13 of their papers we have counts for
8 papers · 1 filter
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,…
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…
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…
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…
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…
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…