Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap

Charita Dellaporta · Jeremias Knoblauch · Theodoros Damoulas · Francois-Xavier Briol

award Best Paper Award
[ Abstract ]
Mon 28 Mar 4:30 a.m. PDT — 6 a.m. PDT
Oral presentation: Oral 2: Bayesian methods / Sampling methods
Mon 28 Mar 2:30 a.m. PDT — 3:30 a.m. PDT


Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena, and as such can often be misspecified in practice. Unfortunately, existing Bayesian approaches for simulators are known to perform poorly in those cases. In this paper, we propose a novel algorithm based on the posterior bootstrap and maximum mean discrepancy estimators. This leads to a highly-parallelisable Bayesian inference algorithm with strong robustness properties. This is demonstrated through an in-depth theoretical study which includes generalisation bounds and proofs of frequentist consistency and robustness of our posterior. The approach is then assessed on a range of examples including a g-and-k distribution and a toggle-switch model.

Chat is not available.