  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.
