Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. (15th May 2023)

• Record Type: Journal Article
• Authors: Bon, Joshua J.; Bretherton, Adam; Buchhorn, Katie; Cramb, Susanna; Drovandi, Christopher; Hassan, Conor; Jenner, Adrianne L.; Mayfield, Helen J.; McGree, James M.; Mengersen, Kerrie; Price, Aiden; Salomone, Robert; Santos-Fernandez, Edgar; Vercelloni, Julie; Wang, Xiaoyu
• Is Part Of: Philosophical transactions. Volume 381:Number 2247(2023)
• Publication Date: 2023-05-15
• Subjects: intelligent data collection -- federated analysis -- new data sources -- implicit models -- model transfer -- Bayesian software products; Physical sciences -- Periodicals; Engineering -- Periodicals; Mathematics -- Periodicals; 500
• DOI: 10.1098/rsta.2022.0156
• Languages: English
• ISSNs: 1364-503X

Abstract

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'.