Modern Bayesian Experimental Design
MicrosoftOxford
arXiv:2302.14545v2195 citationsh-index: 28
AI Analysis
This is an incremental review for researchers in experimental design and Bayesian methods.
The paper reviews Bayesian experimental design (BED), addressing its computational challenges that hinder practical use, and outlines recent advances that improve its effectiveness.
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.