efficient acquisition rules for ABC

A few weeks ago, Marko Järvenpää, Michael Gutmann, Aki Vehtari and Pekka Marttinen arXived a paper on sampling design for ABC that reminded me of presentations Michael gave at NIPS 2014 and in Banff last February. The main notion is that, when the simulation from the model is hugely expensive, random sampling does not make sense.

“While probabilistic modelling has been used to accelerate ABC inference, and strategies have been proposed for selecting which parameter to simulate next, little work has focused on trying to quantify the amount of uncertainty in the estimator of the ABC posterior density itself.”

The above question  is obviously interesting, if already considered in the literature for it seems to focus on the Monte Carlo error in ABC, addressed for instance in Fearnhead and Prangle (2012), Li and Fearnhead (2016) and our paper with David Frazier, Gael Martin, and Judith Rousseau. With corresponding conditions on the tolerance and the number of simulations to relegate Monte Carlo error to a secondary level. And the additional remark that the (error free) ABC distribution itself is not the ultimate quantity of interest. Or the equivalent (?) one that ABC is actually an exact Bayesian method on a completed space.

The paper initially confused me for a section on the very general formulation of ABC posterior approximation and error in this approximation. And simulation design for minimising this error. It confused me as it sounded too vague but only for a while as the remaining sections appear to be independent. The operational concept of the paper is to assume that the discrepancy between observed and simulated data, when perceived as a random function of the parameter θ, is a Gaussian process [over the parameter space]. This modelling allows for a prediction of the discrepancy at a new value of θ, which can be chosen as maximising the variance of the likelihood approximation. Or more precisely of the acceptance probability. While the authors report improved estimation of the exact posterior, I find no intuition as to why this should be the case when focussing on the discrepancy, especially because small discrepancies are associated with parameters approximately generated from the posterior.

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