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

被引：3

作者：

Bon, Joshua J. J. ^{[1
,2
]}

Bretherton, Adam ^{[1
,2
]}

Buchhorn, Katie ^{[1
,2
]}

Cramb, Susanna ^{[1
,4
]}

Drovandi, Christopher ^{[1
,2
]}

Hassan, Conor ^{[1
,2
]}

Jenner, Adrianne L. L. ^{[1
,2
]}

Mayfield, Helen J. J. ^{[1
,5
]}

McGree, James M. M. ^{[1
,2
]}

Mengersen, Kerrie ^{[1
,2
]}

Price, Aiden ^{[1
,2
]}

Salomone, Robert ^{[1
,3
]}

Santos-Fernandez, Edgar ^{[1
,2
]}

Vercelloni, Julie ^{[1
,2
]}

Wang, Xiaoyu ^{[1
,2
]}

机构：

[1] Queensland Univ Technol, Ctr Data Sci, Brisbane, Qld, Australia

[2] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld, Australia

[3] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld, Australia

[4] Queensland Univ Technol, Sch Publ Hlth & Social Work, Brisbane, Qld, Australia

[5] Univ Queensland, Sch Publ Hlth, St Lucia, Qld, Australia

来源：

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2023年 / 381卷 / 2247期

基金：

澳大利亚国家健康与医学研究理事会; 澳大利亚研究理事会;

关键词：

intelligent data collection; federated analysis; new data sources; implicit models; model transfer; Bayesian software products; OPTIMAL-DESIGN; REGRESSION; INFERENCE; NETWORK; ADVANTAGES; TRENDS;

D O I：

10.1098/rsta.2022.0156

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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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页数：29

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