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，其实际部署常常带来巨大的计算挑战，这可能削弱其实际应用价值。本综述将概述近年来取得的最新进展如何显著提升了我们克服这些挑战、从而有效运用BED的能力，并进一步探讨该领域未来发展的若干关键方向。