We have now completed our revision of the paper Relevant statistics for Bayesian model choice, written with Judith Rousseau, Jean-Michel Marin, and Natesh Pillai. It has been resubmitted to Series B and reposted on arXiv. The major change in the paper is the inclusion of a check about the relevance ... [Read more...]

Large-scale Inference by Brad Efron is the first IMS Monograph in this new series, coordinated by David Cox and published by Cambridge University Press. Since I read this book immediately after Cox’ and Donnelly’s Principles of Applied Statistics, I was thinking of drawing a parallel between the two books. ... [Read more...]

Another arXiv posting I had had no time to comment is Nial Friel’s and Jason Wyse’s “Estimating the model evidence: a review“. This is a review in the spirit of two of our papers, “Importance sampling methods for Bayesian discrimination between embedded models” with Jean-Michel Marin (published in ... [Read more...]

As supplementary material to the ABC paper we just arXived, here is the R code I used to produce the Bayes factor comparisons between summary statistics in the normal versus Laplace example. (Warning: running the R code takes a while!) Filed under: R, Statistics, University life Tagged: ABC, Bayesian model ... [Read more...]

Here is [yet!] another Bayesian textbook that appeared recently. I read it in the past few days and, despite my obvious biases and prejudices, I liked it very much! It has a lot in common (at least in spirit) with our Bayesian Core, which may explain why I feel so ... [Read more...]

Here is our discussion (with Nicolas Chopin) of the Read Paper of last Wednesday by T. van Erven, P. Grünwald and S. de Rooij (Centrum voor Wiskunde en Informatica, Amsterdam), entitled Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the Akaike ... [Read more...]

Fig. 4 – Boxplots of the evolution [against ε] of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to [accepted proposals from] models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time [...] [Read more...]

“It seems quite absurd to reject an EP-based approach, if the only alternative is an ABC approach based on summary statistics, which introduces a bias which seems both larger (according to our numerical examples) and more arbitrary, in the sense that in real-world applications one has little intuition and even ... [Read more...]

Monday August 01 was the first full day of JSM 2011 and full is the appropriate word to describe the day! It started for me at 7am with a round table run by Marc Suchard on parallel computing (or at 3am if I am considering the time I woke up!). I was ... [Read more...]

I just got the following email from PNAS about our Lack of confidence in ABC model choice. Editor's Remarks to Author: both referees now find the manuscript acceptable for publication as do I. Each suggests small changes which I encourage the authors to make prior to having the manuscript go ... [Read more...]

Yesterday, we had a meeting of our EMILE network on statistics for population genetics (in Montpellier) and we were discussing our respective recent advances in ABC model choice. One of our colleagues mentioned the constant request (from referees) to include the post-ABC processing devised by Fagundes et al. in their 2007 ... [Read more...]

Following my earlier posts on the revision of Lack of confidence, here is an interesting outcome from the derivation of the exact marginal likelihood in the Laplace case. Computing the posterior probability of a normal model versus a Laplace model in the normal (gold) and the Laplace (chocolate) settings leads ... [Read more...]

In conjunction with the normal-Laplace comparison mentioned in the most recent post about our lack of confidence in ABC model choice, we have been working on the derivation of the exact Bayes factor and I derived an easy formula for the marginal likelihood in the Laplace case that boils down ... [Read more...]

Following the comments on our earlier submission to PNAS, we have written (and re-arXived) a revised version where we try to spell out (better) the distinction between ABC point (and confidence) estimation and ABC model choice, namely that the problem was at another level for Bayesian model choice (using posterior ... [Read more...]

An ‘Og reader. Emmanuel Charpentier, sent me the following email about model choice: I read with great interest your critique of Peter Congdon’s 2006 paper (CSDA, 50(2):346-357) proposing a method of estimation of posterior model probabilities based on improper distributions for parameters not present in the model inder examination, as ... [Read more...]

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A new posting on arXiv by Benedict Escoto on a simulation method for approximating normalising constants (i.e. evidence) with an eye-catching name! Here is the abstract This paper describes a method for estimating the marginal likelihood or Bayes factors of Bayesian models using non-parametric importance sampling (“arrogance sampling”). This ... [Read more...]

Last week, I received a box of books from the International Statistical Review, for reviewing them. I thus grabbed the one whose title was most appealing to me, namely Bayesian Model Selection and Statistical Modeling by Tomohiro Ando. I am indeed interested in both the nature of testing hypotheses or ... [Read more...]

In the continuation of my earlier post on computing evidence, I read a very interesting paper by Merlise Clyde, Joyee Ghosh and Michael Littman, to appear in JCGS. It is called  Bayesian adaptive sampling for variable selection and model averaging. The sound idea at the basis of the paper is ... [Read more...]