688 citations · 1.3k across the 5 of their papers we have counts for
6 papers
Reliable ABC model choice via random forests
Pierre Pudlo, Jean-Michel Marin, Arnaud Estoup +3
Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulat…
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…
Lack of confidence in ABC model choice
Christian P. Robert, Jean-Marie Cornuet, Jean-Michel Marin +1
Approximate Bayesian computation (ABC) have become a essential tool for the analysis of complex stochastic models. Earlier, Grelaud et al. (2009) advocated the use of ABC for Bayes…
Adaptive Multiple Importance Sampling
Jean-Marie Cornuet, Jean-Michel Marin, Antonietta Mira +1
The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earli…
Adaptive approximate Bayesian computation
Mark A. Beaumont, Jean-Marie Cornuet, Jean-Michel Marin +1
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this metho…
Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation
Jean-Marie Cornuet, Filipe Santos, Mark A. Beaumont +5
Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) but they requir…