23 citations · 118 across the 21 of their papers we have counts for
4 papers · 1 filter
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…
Bayesian computation via empirical likelihood
K. L. Mengersen, P. Pudlo, C. P. Robert
Approximate Bayesian computation (ABC) has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However,…
Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation"
Christophe Andrieu, Simon Barthelme, Nicolas Chopin +11
This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply fr…