Title: Simulation-based Bayesian inference under model misspecification Show Chinese title

Title: 基于模拟的模型误设贝叶斯推理

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. Show Chinese abstract

Abstract: 基于仿真推断（SBI）方法广泛用于复杂模型的参数估计，在这些模型中计算似然函数具有挑战性，但生成仿真相对容易。然而，这些方法通常假设仿真模型能够准确反映真实数据生成过程，而这一假设在现实场景中经常被违背。本文聚焦于SBI方法在模型误设下的挑战，并整合了旨在减轻误设影响的最新研究，强调了三种关键策略：i）鲁棒汇总统计量，ii）广义贝叶斯推断，以及iii）误差建模和调整参数。为了展示流行SBI方法的脆弱性以及误设鲁棒替代方法的有效性，我们在一个说明性例子上展示了实证结果。