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Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Harita Dellaporta · Jeremias Knoblauch · Theodoros Damoulas · Francois-Xavier Briol

Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. Such models 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 misspecified settings. In this paper, we propose a novel approach based on the posterior bootstrap which gives a highly-parallelisable Bayesian inference algorithm for simulator-based models. Our approach is based on maximum mean discrepancy estimators, which also allows us to inherit their robustness properties.

Author Information

Harita Dellaporta (University of Warwick)
Jeremias Knoblauch (Warwick University)
Theodoros Damoulas (University of Warwick)
Francois-Xavier Briol (Alan Turing Institute)

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