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Simulation-based Bayesian inference under model misspecification

Fuente: arXiv

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Bibliographic Details
Main Authors:	Kelly, Ryan P., Warne, David J., Frazier, David T., Nott, David J., Gutmann, Michael U., Drovandi, Christopher
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning Computation
Online Access:	Acceder al recurso
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author	Kelly, Ryan P. Warne, David J. Frazier, David T. Nott, David J. Gutmann, Michael U. Drovandi, Christopher
author_facet	Kelly, Ryan P. Warne, David J. Frazier, David T. Nott, David J. Gutmann, Michael U. Drovandi, Christopher
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2503_12315
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Simulation-based Bayesian inference under model misspecification Kelly, Ryan P. Warne, David J. Frazier, David T. Nott, David J. Gutmann, Michael U. Drovandi, Christopher Methodology Machine Learning Computation 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.
title	Simulation-based Bayesian inference under model misspecification
topic	Methodology Machine Learning Computation
url	https://arxiv.org/abs/2503.12315