Modern Bayesian Experimental Design
Machine Learning
2023-11-30 v2 Artificial Intelligence
Machine Learning
Computation
Abstract
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.
Keywords
Cite
@article{arxiv.2302.14545,
title = {Modern Bayesian Experimental Design},
author = {Tom Rainforth and Adam Foster and Desi R Ivanova and Freddie Bickford Smith},
journal= {arXiv preprint arXiv:2302.14545},
year = {2023}
}
Comments
Accepted for publication in Statistical Science