ABC model assessment
We just [arXived and] submitted to Statistics & Computing special issue on ABC a paper on ABC model assessment with Olli Ratman, Pierre Pudlo and Sylvia Richardson. The central idea is to incorporate the errors within the ABC simulation, thanks to an extra prior and a kernel acceptance probability on those errors. The existing ABC algorithms can be modified to this effect. This short paper thus extends the earlier PNAS paper by Olli et al. to include the MH and SIS ABC extensions, and to test those on several applications: network, dynamical systems, and population genetics. In the later case, while the ABC estimated Bayes factor agrees with an importance sampling approximation, we show that the measure of discrepancy provided by our approach highlights a poor fit of both models under comparison. Although we (Natesh Pillai, Jean-Michel Marin, Judith Rousseau and myself) are currently making progress towards an indicator of worth for the ABC Bayes factor approximation, these exploratory tools are quite valuable to defend an ABC approach to model evaluation and to model choice.