In one of the presentations by the last cohort of OxWaSP students, the group decided to implement an ABC model choice strategy based on sequential ABC inspired from Toni et al. (2008). and this made me reconsider this approach (disclaimer: no criticism of the students implied in the following!). Indeed, the outcome of the simulation […]

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## particular degeneracy in ABC model choice

February 22, 2019## ABC model choice via random forests accepted!

October 21, 2015“This revision represents a very nice response to the earlier round of reviews, including a significant extension in which the posterior probability of the selected model is now estimated (whereas previously this was not included). The extension is a very nice one, and I am happy to see it included.” Anonymous Great news [at least […]

## ABC model choice via random forests [and no fire]

September 4, 2015While my arXiv newspage today had a puzzling entry about modelling UFOs sightings in France, it also broadcast our revision of Reliable ABC model choice via random forests, version that we resubmitted today to Bioinformatics after a quite thorough upgrade, the most dramatic one being the realisation we could also approximate the posterior probability of […]

## reliable ABC model choice via random forests

October 29, 2014After a somewhat prolonged labour (!), we have at last completed our paper on ABC model choice with random forests and submitted it to PNAS for possible publication. While the paper is entirely methodological, the primary domain of application of ABC model choice methods remains population genetics and the diffusion of this new methodology to […]

## insufficient statistics for ABC model choice

October 17, 2014[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, […]