发表机构
Monash University（莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对贝叶斯分析中似然与数据生成不匹配的模型误设问题，通过回顾后验的变分解释提供替代依据，并介绍相关广义贝叶斯推断技术以解决该问题。
AI 中文摘要
贝叶斯分析中使用的似然输入几乎从未完全对应数据的生成方式，这使后验推断的有效性受到质疑。本文回顾了贝叶斯后验的变分解释，将其作为模型误设下使用贝叶斯方法的替代依据，并探讨其对不确定性量化和参数估计的影响。随后，本文介绍了一系列旨在考虑模型误设的广义贝叶斯推断技术。
英文摘要
The likelihood input to a Bayesian analysis almost never exactly represents how the data were generated, calling into question the validity of posterior inferences. We review a variational interpretation of the Bayesian posterior as an alternative justification for its use under model misspecification, and consider the resulting implications on uncertainty quantification and parameter estimation. We then introduce a range of techniques that seek to obtain generalised Bayesian inferences that account for model misspecification
Journal refJewson, J. (2026). Bayesian Inference Under Model Misspecification. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels)
DOI：10.1002/9781118445112.stat08674
