Simulation-based Bayesian inference under model misspecification
This addresses a critical limitation for researchers and practitioners using SBI in fields like computational biology or cosmology, where model assumptions are often violated, but it is incremental as it reviews and synthesizes existing approaches rather than introducing a new method.
The paper tackles the problem of simulation-based Bayesian inference (SBI) methods failing under model misspecification, where the simulation model does not match the true data-generating process, and consolidates strategies like robust summary statistics to address this issue, though no concrete numerical results are provided in the 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.