Category: Approximate Bayesian computation

ABC with Gibbs steps

With Grégoire Clarté, Robin Ryder and Julien Stoehr, all from Paris-Dauphine, we have just arXived a paper on the specifics of ABC-Gibbs, which is a version of ABC where the generic ABC accept-reject step is replaced by a sequence of n conditional ABC accept-reject steps, each aiming at an ABC version of a conditional distribution […]

holistic framework for ABC

An AISTATS 2019 paper was recently arXived by Kelvin Hsu and Fabio Ramos. Proposing an ABC method “…consisting of (1) a consistent surrogate likelihood model that modularizes queries from simulation calls, (2) a Bayesian learning objective for hyperparameters that improves inference accuracy, and (3) a posterior surrogate density and a super-sampling inference algorithm using its […]

prepaid ABC

Merijn Mestdagha, Stijn Verdoncka, Kristof Meersa, Tim Loossensa, and Francis Tuerlinckx from the KU Leuven, some of whom I met during a visit to its Wallon counterpart Louvain-La-Neuve, proposed and arXived a new likelihood-free approach based on saving simulations on a large scale for future users. Future users interested in the same model. The very […]

a book and three chapters on ABC

In connection with our handbook on mixtures being published, here are three chapters I contributed to from the Handbook of ABC, edited by Scott Sisson, Yanan Fan, and Mark Beaumont: 6. Likelihood-free Model Choice, by J.-M. Marin, P. Pudlo, A. Estoup and C.P. Robert 12. Approximating the Likelihood in ABC, by  C. C. Drovandi, C. […]

approximate likelihood perspective on ABC

George Karabatsos and Fabrizio Leisen have recently published in Statistics Surveys a fairly complete survey on ABC methods [which earlier arXival I had missed]. Listing within an extensive bibliography of 20 pages some twenty-plus earlier reviews on ABC (with further ones in applied domains)! “(…) any ABC method (algorithm) can be categorized as either (1) […]