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 areas for future development in the field.
Modern Bayesian experimental design
T. Rainforth, A. Foster, D. R. Ivanova and F. Bickford Smith
Published: 01/02/2024
Published in:
Statistical Science
Statistical Science