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).
Preview |
Text
2202.04744v1.pdf - Accepted Version Download (1MB) | Preview |
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 |
Archive Staff Only
![]() |
View Item |