Surrogate models - also called emulators - are widely used to facilitate Bayesian inference in settings where computational costs preclude the use of standard posterior inference algorithms. Their deployment is now standard practice across many scientific domains. However, integrating surrogates in statistical analyses introduces unique challenges that complicate established Bayesian workflow principles. While significant progress has been made in addressing these issues, the relevant developments are scattered across several distinct research communities, with different emphases and perspective. We present a unifying review that synthesizes the literature into a coherent framework, aiming to benefit both practitioners and methods developers. We place particular emphasis on propagating surrogate uncertainty and sequentially refining emulators via active learning, two key components of a robust surrogate-based Bayesian workflow.

翻译：代理模型（亦称仿真器）在计算成本阻碍标准后验推断算法应用的场景中被广泛用于促进贝叶斯推断。其部署现已成为众多科学领域的标准实践。然而，在统计分析中集成代理模型会引入独特的挑战，这些挑战使得既定的贝叶斯工作流原则复杂化。尽管在解决这些问题方面已取得显著进展，但相关研究成果分散在多个不同的研究社群中，各自侧重点与视角各异。本文提出一种统一性综述，将现有文献整合为连贯的框架，旨在为实践者与方法开发者提供参考。我们特别关注代理不确定性的传播以及通过主动学习对仿真器进行序贯优化——这两者是构建稳健的基于代理模型的贝叶斯工作流的关键组成部分。