**T**oday I received in the mail a copy of the short book published by edp sciences after the courses we gave last year at the astrophysics summer school, in Autrans. Which contains a quick introduction to ABC extracted from my notes (which I still hope to turn into a book!). As well as a longer coverage of Bayesian foundations and computations by David Stenning and David van Dyk.

## Archive for computational astrophysics

## ABC intro for Astrophysics

Posted in Books, Kids, Mountains, R, Running, Statistics, University life with tags ABC, Approximate Bayesian computation, Autrans, Bayesian foundations, Bayesian methodology, Book, computational astrophysics, review, Statistics for Astrophysics, summer course, survey, Vercors on October 15, 2018 by xi'an## optimal proposal for ABC

Posted in Statistics with tags ABC, ABC-PMC, ABC-SMC, adaptive importance sampling, Bayesian Analysis, computational astrophysics, effective sample size, Ewan Cameron, kernel density estimator, Kullback-Leibler divergence, mixtures of distributions on October 8, 2018 by xi'an**A**s pointed out by Ewan Cameron in a recent c’Og’ment, Justin Alsing, Benjamin Wandelt, and Stephen Feeney have arXived last August a paper where they discuss an optimal proposal density for ABC-SMC and ABC-PMC. Optimality being understood as maximising the effective sample size.

“Previous studies have sought kernels that are optimal in the (…) Kullback-Leibler divergence between the proposal KDE and the target density.”

The effective sample size for ABC-SMC is actually the regular ESS multiplied by the fraction of accepted simulations. Which surprisingly converges to the ratio

**E**[q(θ)/π(θ)|**D**]/**E**[π(θ)/q(θ)|**D**]

under the (true) posterior. (Where q(θ) is the importance density and π(θ) the prior density.] When optimised in q, this usually produces an implicit equation which results in a form of geometric mean between posterior and prior. The paper looks at approximate ways to find this optimum. Especially at an upper bound on q. Something I do not understand from the simulations is that the starting point seems to be the plain geometric mean between posterior and prior, in a setting where the posterior is supposedly unavailable… Actually the paper is silent on how the optimal can be approximated in practice, for the very reason I just mentioned. Apart from using a non-parametric or mixture estimate of the posterior after each SMC iteration, which may prove extremely costly when processed through the optimisation steps. However, an interesting if side outcome of these simulations is that the above geometric mean does much better than the posterior itself when considering the effective sample size.