🤖 AI Summary
This work addresses the challenge of performing Bayesian inference in high-computational-cost settings, where standard approaches are often infeasible and existing surrogate modeling methods frequently neglect uncertainty propagation and workflow integration. The paper introduces the first unified framework that systematically integrates surrogate modeling, Bayesian inference, uncertainty quantification, and active learning. By co-designing surrogate uncertainty modeling with sequential optimization mechanisms, the framework enables an efficient and robust inference pipeline. It synthesizes previously fragmented research across multiple domains into a coherent methodology, offering practitioners and method developers a clear, actionable surrogate-based Bayesian workflow that substantially enhances both the reliability and efficiency of inference in expensive simulation scenarios.
📝 Abstract
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.