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

Dellaporta, Charita; Knoblauch, Jeremias; Damoulas, Theodoros; Briol, François-Xavier; (2022) Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap. In: AISTATS 2022 Accepted Papers. AISTATS (In press). Green open access

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Abstract

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.

Type: Proceedings paper
Title: Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Event: AISTATS 2022
Open access status: An open access version is available from UCL Discovery
Publisher version: http://aistats.org/aistats2022/accepted.html
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10143633
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