Journal article

Modern Bayesian experimental design

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 areas for future development in the field.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Rainforth, T., Foster, A., Ivanova, D. R., & Bickford Smith, F. (2024). Modern Bayesian experimental design. Statistical Science, 39(1), 100–114 .

MLA Style

Rainforth, T., et al. “Modern Bayesian Experimental Design.” Statistical Science, vol. 39, no. 1, Institute of Mathematical Statistics, 2024, pp. 100–14 .

Chicago Style

Rainforth, T, A Foster, DR Ivanova, and F Bickford Smith. 2024. “Modern Bayesian Experimental Design.” Statistical Science 39 (1): 100–114 .
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Files:: Rainforth_et_al_2024_Modern_Bayesian_experimental.pdf

(Preview, Accepted manuscript, pdf, 361.9KB, Terms of use)

Publisher copy:: 10.1214/23-sts915

Authors

+ Rainforth, T More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Statistics
Role:: Author

+ Foster, A More by this author

Role:: Author

+ Ivanova, DR More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Statistics
Role:: Author

+ Bickford Smith, F More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Computer Science
Role:: Author

Publisher:: Institute of Mathematical Statistics
Journal:: Statistical Science More from this journal
Volume:: 39
Issue:: 1
Pages:: 100-114
Publication date:: 2024-02-18
Acceptance date:: 2023-11-04
DOI:: 10.1214/23-sts915
ISSN:: 0883-4237

Language:: English
Keywords:: Bayesian adaptive design

Bayesian optimal design

information maximization

active learning
Pubs id:: 1855979
Local pid:: pubs:1855979
Deposit date:: 2024-03-20

Terms of use

Copyright holder:: Rainforth et al
Copyright date:: 2024
Rights statement:: © 2024 The Authors. This research was funded, in whole or in part, by EPSRC, EP/SO23151/1. A CC BY 4.0 license is applied to this article arising from this submission, in accordance with the grant’s open access conditions.
Notes:: This is the accepted manuscript version of the article. The final version is available from Institute of Mathematical Statistics at: 10.1214/23-STS9150

Licence:: CC Attribution (CC BY)

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