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’.
Model transfer is the task of using information from source domains to improve inference on a target domain with limited data. In real applications it is unclear when to transfer information, which information to transfer and how to transfer this information. For example, consider modelling an invasive species that requires a time-sensitive solution. Here, waiting for more data can be detrimental, necessitating model transfer from previous studies. We develop a new mathematical and computational framework, implementing transfer for statistical models where it was previously not possible. This new framework permitted an extensive comparison of different approaches for model transfer.
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