43 citations · 242 across the 46 of their papers we have counts for
9 papers · 1 filter
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…
Bayesian inference, model selection and likelihood estimation using fast rejection sampling: the Conway-Maxwell-Poisson distribution
Alan Benson, Nial Friel
Bayesian inference for models with intractable likelihood functions represents a challenging suite of problems in modern statistics. In this work we analyse the Conway-Maxwell-Pois…
Noisy Hamiltonian Monte Carlo for doubly-intractable distributions
Julien Stoehr, Alan Benson, Nial Friel
Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician's toolbox as an alternative sampling method in settings when standard Metropolis-Hastings…
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…