Simulation-based Bayesian inference under model misspecification

• URL: http://arxiv.org/abs/2503.12315v1
• Date: Sun, 16 Mar 2025 01:47:19 GMT
• Title: Simulation-based Bayesian inference under model misspecification
• Authors: Ryan P. Kelly, David J. Warne, David T. Frazier, David J. Nott, Michael U. Gutmann, Christopher Drovandi,
• Abstract summary: We focus on the challenges faced by SBI methods under model misspecification.<n>We consolidate recent research aimed at mitigating the effects of misspecification.<n>To illustrate both the vulnerabilities of popular SBI methods and the effectiveness of misspecification-robust alternatives, we present empirical results.
• Score: 4.2490325931915285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulations is relatively straightforward. However, these methods commonly assume that the simulation model accurately reflects the true data-generating process, an assumption that is frequently violated in realistic scenarios. In this paper, we focus on the challenges faced by SBI methods under model misspecification. We consolidate recent research aimed at mitigating the effects of misspecification, highlighting three key strategies: i) robust summary statistics, ii) generalised Bayesian inference, and iii) error modelling and adjustment parameters. To illustrate both the vulnerabilities of popular SBI methods and the effectiveness of misspecification-robust alternatives, we present empirical results on an illustrative example.

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