insufficient statistics for ABC model choice

[Here is a revised version of my comments on the paper by Julien Stoehr, Pierre Pudlo, and Lionel Cucala, now to appear [both paper and comments] in Statistics and Computing special MCMSki 4 issue.] Approximate Bayesian computation techniques are 2000’s successors of MCMC methods as handling new models where MCMC algorithms are at a loss, […]

insufficient statistics for ABC model choice

Julien Stoehr, Pierre Pudlo, and Lionel Cucala (I3M, Montpellier) arXived yesterday a paper entitled “Geometric summary statistics for ABC model choice between hidden Gibbs random fields“. Julien had presented this work at the MCMski 4 poster session.  The move to a hidden Markov random field means that our original approach with Aude Grelaud does not […]

ABC and sufficient statistics

Chris Barnes, Sarah Filippi, Michael P.H. Stumpf, and Thomas Thorne posted a paper on arXiv on the selection of sufficient statistics towards ABC model choice. This paper, called Considerate Approaches to Achieving Sufficiency for ABC model selection, was presented by Chris Barnes during ABC in London two months ago. (Note that all talks of the meeting […]

Sufficient statistics for ABC

The issue of picking quasi-sufficient statistics in ABC algorithms is quite important and Joyce and Marjoram have published (last August, sorry I’m late!) a proposal in Statistical Applications in Genetics and Molecular Biology that is addressing this issue, at least to the extend that the inclusion of a new statistics within the set of summary […]

A precursor of ABC-Gibbs

Following our arXival of ABC-Gibbs, Dennis Prangle pointed out to us a 2016 paper by Athanasios Kousathanas, Christoph Leuenberger, Jonas Helfer, Mathieu Quinodoz, Matthieu Foll, and Daniel Wegmann, Likelihood-Free Inference in High-Dimensional Model, published in Genetics, Vol. 203, 893–904 in June 2016. This paper contains a version of ABC Gibbs where parameters are sequentially simulated […]