Handbooks [not a book review]

a book and three chapters on ABC

In connection with our handbook on mixtures being published, here are three chapters I contributed to from the Handbook of ABC, edited by Scott Sisson, Yanan Fan, and Mark Beaumont:

6. Likelihood-free Model Choice, by J.-M. Marin, P. Pudlo, A. Estoup and C.P. Robert

12. Approximating the Likelihood in ABC, by  C. C. Drovandi, C. Grazian, K. Mengersen and C.P. Robert

17. Application of ABC to Infer about the Genetic History of Pygmy Hunter-Gatherers Populations from Western Central Africa, by A. Estoup, P. Verdu, J.-M. Marin, C. Robert, A. Dehne-Garcia, J.-M. Cornuet and P. Pudlo

ABC in print

The CRC Press Handbook of ABC is now out, after a rather long delay [the first version of our model choice chapter was written in 2015!] due to some late contributors Which is why I did not spot it at JSM 2018. As announced a few weeks ago, our Handbook of Mixture Analysis is soon to be published as well. (Not that I necessarily advocate the individual purchase of these costly volumes!, especially given most chapters are available on-line.)

ABCDay [arXivals]

A bunch of ABC papers on arXiv yesterday, most of them linked to the incoming Handbook of ABC:

1. 1. Overview of Approximate Bayesian Computation S. A. Sisson, Y. Fan, M. A. Beaumont
   2. Kernel Recursive ABC: Point Estimation with Intractable Likelihood Takafumi Kajihara, Keisuke Yamazaki, Motonobu Kanagawa, Kenji Fukumizu
   3. High-dimensional ABC D. J. Nott, V. M.-H. Ong, Y. Fan, S. A. Sisson
   4. ABC Samplers Y. Fan, S. A. Sisson

 

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…