Adversarial Bayesian Simulation [One World ABC’minar]

The next One World ABC webinar will take place on 24 November, at 1:30 UK Time (GMT) and will be presented by Yi Yuexi Wang (University of Chicago) on “Adversarial Bayesian Simulation”, available on arXiv. [The link to the webinar is available to those who have registered.]

In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. In this talk, we will cover two summary-free ABC approaches, both inspired by adversarial learning. The first one adopts a classification-based KL estimator to quantify the discrepancy between real and simulated datasets. We consider the traditional accept/reject kernel as well as an exponential weighting scheme which does not require the ABC acceptance threshold. In the second paper, we develop a Bayesian GAN (B-GAN) sampler that directly targets the posterior by solving an adversarial optimization problem. B-GAN is driven by a deterministic mapping learned on the ABC reference by conditional GANs. Once the mapping has been trained, iid posterior samples are obtained by filtering noise at a negligible additional cost. We propose two post-processing local refinements using (1) data-driven proposals with importance reweighting, and (2) variational Bayes. For both methods, we support our findings with frequentist-Bayesian theoretical results and highly competitive performance in empirical analysis. (Joint work with Veronika Rockova)

One World ABC Seminar [3rd season]

The third season of the [now monthly] One World ABC Seminar is starting! With talks by

30 September: Matias Quiroz

28 October: Michael Gutmann

25 November: Clara Grazian

27 January: Rafael Izbicki

Thanks to Massimiliano Tamborino (Warwick) to running this seminar! (Register if you have not yet done so.)

rare ABC [webinar impressions]

A second occurrence of the One World ABC seminar by Ivis Kerama, and Richard Everitt (Warwick U), on their on-going pape with and Tom Thorne, Rare Event ABC-SMC², which is not about rare event simulation but truly about ABC improvement. Building upon a previous paper by Prangle et al. (2018). And also connected with Dennis’ talk a fortnight ago in that it exploits an autoencoder representation of the simulated outcome being H(u,θ). It also reminded me of an earlier talk by Nicolas Chopin.

This approach avoids using summary statistics (but relies on a particular distance) and implements a biased sampling of the u’s to produce outcomes more suited to the observation(s). Almost sounds like a fiducial ABC! Their stopping rule for decreasing the tolerance is to spot an increase in the variance of the likelihood estimates. As the method requires many data generations for a single θ, it only applies in certain settings. The ABC approximation is indeed used as an estimation of likelihood ratio (which makes sense for SMC² but is biased because of ABC). I got slightly confused during Richard’s talk by his using the term of unbiased estimator of the likelihood before I realised he was talking of the ABC posterior. Thanks to both speakers, looking forward the talk by Umberto Picchini in a fortnight (on a joint paper with Richard).