688 citations · 1.8k across the 36 of their papers we have counts for
19 papers · 1 filter
Computational Solutions for Bayesian Inference in Mixture Models
Gilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli +2
This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the…
Hidden Gibbs random fields model selection using Block Likelihood Information Criterion
Julien Stoehr, Jean-Michel Marin, Pierre Pudlo
Performing model selection between Gibbs random fields is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot…
Consistency of the Adaptive Multiple Importance Sampling
Jean-Michel Marin, Pierre Pudlo, Mohammed Sedki
Among Monte Carlo techniques, the importance sampling requires fine tuning of a proposal distribution, which is now fluently resolved through iterative schemes. The Adaptive Multip…
Efficient learning in ABC algorithms
Mohammed Sedki, Pierre Pudlo, Jean-Michel Marin +2
Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the post…
Bounding rare event probabilities in computer experiments
Yves Auffray, Pierre Barbillon, Jean-Michel Marin
We are interested in bounding probabilities of rare events in the context of computer experiments. These rare events depend on the output of a physical model with random input vari…
Why approximate Bayesian computational (ABC) methods cannot handle model choice problems
Christian Robert, Jean-Michel Marin, Natesh S. Pillai
Approximate Bayesian computation (ABC), also known as likelihood-free methods, have become a favourite tool for the analysis of complex stochastic models, primarily in population g…