likelihood-free model choice

Jean-Michel Marin, Pierre Pudlo and I just arXived a short review on ABC model choice, first version of a chapter for the incoming Handbook of Approximate Bayesian computation edited by Scott Sisson, Yannan Fan, and Mark Beaumont. Except for a new analysis of a Human evolution scenario, this survey mostly argues for the proposal made in our recent paper on the use of random forests and [also argues] about the lack of reliable approximations to posterior probabilities. (Paper that was rejected by PNAS and that is about to be resubmitted. Hopefully with a more positive outcome.) The conclusion of the survey is  that

The presumably most pessimistic conclusion of this study is that the connections between (i) the true posterior probability of a model, (ii) the ABC version of this probability, and (iii) the random forest version of the above, are at best very loose. This leaves open queries for acceptable approximations of (i), since the posterior predictive error is instead an error assessment for the ABC RF model choice procedure. While a Bayesian quantity that can be computed at little extra cost, it does not necessarily compete with the posterior probability of a model.

reflecting my hope that we can eventually come up with a proper approximation to the “true” posterior probability…

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