Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Applications
2026-01-07 v2
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.
Keywords
Cite
@article{arxiv.2211.10029,
title = {Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics},
author = {Joshua J. Bon and Adam Bretherton and Katie Buchhorn and Susanna Cramb and Christopher Drovandi and Conor Hassan and Adrianne L. Jenner and Helen J. Mayfield and James M. McGree and Kerrie Mengersen and Aiden Price and Robert Salomone and Edgar Santos-Fernandez and Julie Vercelloni and Xiaoyu Wang},
journal= {arXiv preprint arXiv:2211.10029},
year = {2026}
}
Comments
27 pages, 8 figures