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

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.

27 pages, 8 figures

References in corpus (6)

• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
• Flexible statistical inference for mechanistic models of neural dynamics
• Normalized power priors always discount historical data
• A Study on the Power Parameter in Power Prior Bayesian Analysis
• Differentially Private Markov Chain Monte Carlo
• Power priors for replication studies