研究复杂模型下参数推断在模型不匹配时的失效问题，提出三类稳健解决方案。
Simulation-based Bayesian inference under model misspecification
- 采用鲁棒统计量、广义贝叶斯与误差建模三类策略应对模型偏差
- 实证显示主流SBI方法在模型错误时严重失准，而新方法显著提升稳定性
- 适合从事复杂系统建模与贝叶斯推断的研究者参考
模拟驱动的贝叶斯推断（SBI）方法广泛应用于难以计算似然但可生成模拟数据的复杂模型中。然而，这些方法通常假设模拟模型能准确反映真实数据生成过程，这一假设在实际中常被违反。本文聚焦于模型不匹配下的SBI挑战，整合近期研究以缓解此类问题，提出三大关键策略：i）鲁棒摘要统计量，ii）广义贝叶斯推断，iii）误差建模与调整参数。通过一个示范性案例的实证结果，展示了主流SBI方法在模型不匹配时的脆弱性，以及稳健替代方案的有效性。
原文摘要 · Abstract (English)
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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