## potentially relevant

**T**his week, freshly back from Roma, I got the reviews on our paper “Relevant statistics for Bayesian model choice” from Series B. The comments are detailed and mostly to the point, expressing concern about the relevance of the paper for statistical methodology as the major issue. We are thus asked for a revision making a much better connection with ABC methodology.

**T**his is not an unexpected outcome, from my point of view, because the paper is indeed quite theoretical and the mathematical assumptions required to obtain the convergence theorems are rather overwhelming… Meaning that in practical cases they cannot truly be checked. However, I think we can eventually address those concerns for two distinct reasons: first, the paper comes as a third step in a series of papers where we first identified a sufficiency property, then realised that this property was actually quite a rare occurrence, and finally made a theoretical advance as to when is a summary statistic enough (i.e. “sufficient” in the standard sense of the term!) to conduct model choice, with a clear answer that the mean ranges of the summary statistic under each model could not intersect. Second, my own personal view is that those assumptions needed for convergence are not of the highest importance for statistical practice (even though they are needed in the paper!) and thus that, from a methodological point of view, only the conclusion should be taken into account. It is then rather straightforward to come up with (quick-and-dirty) simulation devices to check whether a summary statistic behaves differently under both models, taking advantage of the reference table already available (instead of having to run Monte Carlo experiments with ABC basis)…

**O**ne of the comments was that maybe Bayes factors were not appropriate for conducting model choice, thus making the whole derivation irrelevant. This is a possible perspective but it can be objected that Bayes factors and posterior probabilities are used in conjunction with ABC in dozens of genetic papers. Further arguments are provided in the various replies to both of Templeton’s radical criticisms. That more empirical and model-based assessments also are available is quite correct, as demonstrated in the multicriterion approach of Olli Ratmann and co-authors. This is simply another approach, not followed by most geneticists so far…

May 8, 2012 at 12:14 am

[...] have now completed our revision of the paper Relevant statistics for Bayesian model choice, written with Judith [...]

April 30, 2012 at 12:12 am

[...] now discussed on Cross Validated!). When rerunning simulations to produce nicer graphical outcomes (for the revision), I noticed a much longer run time associated with the computation of the mad statistic. Here is a [...]

April 18, 2012 at 1:15 pm

[...] Here are the (revised) slides of my talk this afternoon at the Confronting Intractability in Statistical Inference workshop in Bristol, supported by SuSTain. The novelty is in the final part, where we managed to apply our result to a three population genetic escenario using one versus two δμ summary statistics. This should be the central new example in the incoming revision of our paper to Series B. [...]

March 29, 2012 at 12:12 am

[...] Bill Strawderman. And my former PhD student Aude Grelaud. Both talks will cover the same ground of ABC model choice and Bayesian consistency (surprise, surprise!). The format of the econometrics seminar at Princeton [...]

March 22, 2012 at 4:43 am

[...] Og, Christian Robert reviews a preprint on resampling and GPU parallelism and shares some thoughts after a referee report for one of his own papers comes [...]

March 19, 2012 at 12:12 am

[...] is a recent email Jean-Michel Marin got about our ABC model choice paper: Dear [...]