Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

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hdl_148141.pdf (1.49 MB)
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Date

2023

Authors

Bon, J.J.
Bretherton, A.
Buchhorn, K.
Cramb, S.
Drovandi, C.
Hassan, C.
Jenner, A.L.
Mayfield, H.J.
McGree, J.M.
Mengersen, K.

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Journal article

Citation

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023; 381(2247):20220156-1-20220156-29

Statement of Responsibility

Joshua J. Bon, Adam Bretherton, Katie Buchhorn, Susanna Cramb, Christopher Drovandi, Conor Hassan, Adrianne L. Jenner, Helen J. Mayfield, James M. McGree, Kerrie Mengersen, Aiden Price, Robert Salomone, Edgar Santos-Fernandez, Julie Vercelloni, and Xiaoyu Wang

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DOI

10.1098/rsta.2022.0156

Abstract

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

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© 2023 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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http://purl.org/au-research/grants/arc/DP200102101
 http://purl.org/au-research/grants/arc/LP180101151
 http://purl.org/au-research/grants/nhmrc/2008313
 http://purl.org/au-research/grants/arc/FT210100260
 http://purl.org/au-research/grants/arc/LP200100468
 http://purl.org/au-research/grants/arc/DP200101263

Published Version

https://doi.org/10.1098/rsta.2022.0156

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Persistent link to this record

https://hdl.handle.net/2440/148141