Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, before discussing some key areas for future development in the field.

翻译：贝叶斯实验设计（BED）为优化实验设计提供了强大且通用的框架。然而，其实际部署常面临显著的计算挑战，从而削弱其实用价值。本综述首先概述了近年来的突破性进展如何显著提升我们应对这些挑战并有效运用BED的能力，进而探讨了该领域未来发展的若干关键方向。