![ABC model choice via random forests [and no fire]](https://xianblog.files.wordpress.com/2012/04/dscn2914.jpg?w=254&h=452)
While my arXiv newspage today had a puzzling entry about modelling UFOs sightings in France, it also broadcast our revision of Reliable ABC model choice via random forests, version that we resubmitted today to Bioinformatics after a quite thorough upgrade, the most dramatic one being the realisation we could also approximate the posterior probability of […]
In conjunction with our reliable ABC model choice via random forest paper, about to be resubmitted to Bioinformatics, we have contributed an R package called abcrf that produces a most likely model and its posterior probability out of an ABC reference table. In conjunction with the realisation that we could devise an approximation to the […]
After 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 […]
![ABC model choice by random forests [guest post]](http://xianblog.files.wordpress.com/2009/08/treerise6.jpg?w=311&h=233)
[Dennis Prangle sent me his comments on our ABC model choice by random forests paper. Here they are! And I appreciate very much contributors commenting on my paper or others, so please feel free to join.] This paper proposes a new approach to likelihood-free model choice based on random forest classifiers. These are fit to […]
After more than a year of collaboration, meetings, simulations, delays, switches, visits, more delays, more simulations, discussions, and a final marathon wrapping day last Friday, Jean-Michel Marin, Pierre Pudlo, and I at last completed our latest collaboration on ABC, with the central arguments that (a) using random forests is a good tool for choosing the […]
