news from PCI

locusts in a random forest

My friends from Montpellier, where I am visiting today, Arnaud Estoup, Jean-Michel Marin, and Louis Raynal, along with their co-authors, have recently posted on biorXiv a paper using ABC-RF (Random Forests) to analyse the divergence of two populations of desert locusts in Africa. (I actually first heard of their paper by an unsolicited email from one of these self-declared research aggregates.)

“…the present study is the first one using recently developed ABC-RF algorithms to carry out inferences about both scenario choice and parameter estimation, on a real multi-locus microsatellite dataset. It includes and illustrates three novelties in statistical analyses (…): model grouping analyses based on several key evolutionary events, assessment of the quality of predictions to evaluate the robustness of our inferences, and incorporation of previous information on the mutational setting of the used microsatellite markers”.

The construction of the competing models (or scenarios) is built upon data of past precipitations and desert evolution spanning several interglacial periods, back to the middle Pleistocene, concluding at a probable separation in the middle-late stages of the Holocene, which corresponds to the last transition from humid to arid conditions in the African continent. The probability of choosing the wrong model is exploited to determine which model(s) lead(s) to a posterior [ABC] probability lower than the corresponding prior probability, and only one scenario stands this test. As in previous ABC-RF implementations, the summary statistics are complemented by pure noise statistics in order to determine a barrier in the collection of statistics, even though those just above the noise elements (which often cluster together) may achieve better Gini importance by mere chance. An aspect of the paper that I particularly like is the discussion of the various prior modellings one can derive from existing information (or lack thereof) and the evaluation of the impact of these modellings on the resulting inference based on simulated pseudo-data.

likelihood free nested sampling

A recent paper by Mikelson and Khammash found on bioRxiv considers the (paradoxical?) mixture of nested sampling and intractable likelihood. They however cover only the case when a particle filter or another unbiased estimator of the likelihood function can be found. Unless I am missing something in the paper, this seems a very costly and convoluted approach when pseudo-marginal MCMC is available. Or the rather substantial literature on computational approaches to state-space models. Furthermore simulating under the lower likelihood constraint gets even more intricate than for standard nested sampling as the parameter space is augmented with the likelihood estimator as an extra variable. And this makes a constrained simulation the harder, to the point that the paper need resort to a Dirichlet process Gaussian mixture approximation of the constrained density. It thus sounds quite an intricate approach to the problem. (For one of the realistic examples, the authors mention a 12 hour computation on a 48 core cluster. Producing an approximation of the evidence that is not unarguably stabilised, contrary to the above.) Once again, not being completely up-to-date in sequential Monte Carlo, I may miss a difficulty in analysing such models with other methods, but the proposal seems to be highly demanding with respect to the target.