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Abstract

The Bayesian paradigm was fundamental to George Box's philosophy of statistics. Box's scholarship in statistics was driven by his engagement with other scientists in the process of scientific discovery. In his view, scientific discovery was represented elegantly by Bayes' theorem, in which information from the latest experiment is combined with current knowledge. Applications to real problems was the focus of his research in Bayesian methods, especially problems that were less accessible to classical methods based on sampling theory. These problems typically related to the design of experiments and analysis of experimental data, hierarchical models, the sensitivity of inferences to assumptions about the data, and the use of non-informative priors. His work with a network of collaborators laid the groundwork for widespread application of Bayesian methods facilitated by later advances in computational methods. Copyright © 2014 John Wiley & Sons, Ltd.