In a cross-validated forum exchange, I used the code below to illustrate the working of an ABC algorithm: Hence I used the median and the mad as my summary statistics. And the outcome is rather surprising, for two reasons: the first one is that the posterior on the mean μ is much wider than […]

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## an ABC experiment

November 24, 2014## 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, […]

## ABC model choice via random forests [expanded]

October 1, 2014Today, we arXived a second version of our paper on ABC model choice with random forests. Or maybe [A]BC model choice with random forests. Since the random forest is built on a simulation from the prior predictive and no further approximation is used in the process. Except for the computation of the posterior [predictive] error […]

## ABC@NIPS: call for papers

September 9, 2014In connection with the previous announcement of ABC in Montréal, a call for papers that came out today: NIPS 2014 Workshop: ABC in Montreal December 12, 2014 Montréal, Québec, Canada Approximate Bayesian computation (ABC) or likelihood-free (LF) methods have developed mostly beyond the radar of the machine learning community, but are important tools for a […]