Perspectives on Bayesian Inference and Their Implications for Data Analysis

Use of Bayesian methods has proliferated in recent years as technological and software developments have made Bayesian methods more approachable for researchers working with empirical data. Connected with the increased usage of Bayesian methods in empirical studies is a corresponding increase in recommendations and best practices for Bayesian methods. However, given the extensive scope of Bayes, theorem, there are various compelling perspectives one could adopt for its application. This paper first describes five different perspectives, including examples of different methodologies that are aligned within these perspectives. We then discuss how the different perspectives can have implications for modeling and reporting practices, such that approaches and recommendations that are perfectly reasonable under one perspective might be unreasonable when viewed from another perspective. The ultimate goal is to show the heterogeneity of defensible practices in Bayesian methods and to foster a greater appreciation for the variety of orientations that exist. Translational AbstractBayesian inference and data analytic methods have received increasing attention in recent years. However, there are various perspectives one could adopt for the application of Bayesian inference. This article first describes five different perspectives on Bayesian inference, including examples of different methodologies that are entrenched within particular perspectives. We also discuss how these different perspectives can have differing implications for modeling and reporting practices such that approaches and recommendations that are perfectly reasonable under one perspective might be unreasonable when viewed from another perspective. The ultimate goal is to highlight the variety of perspectives that researchers adopt when employing Bayesian methods.