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Bayesian methods are increasingly popular in research. This paper highlights five distinct perspectives on applying Bayesian methods, showing how different viewpoints impact modeling and reporting practices.

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Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Bayesian methods usage has grown due to technological advancements.
  • This has led to increased recommendations and best practices for Bayesian data analysis.

Purpose of the Study:

  • To describe five distinct perspectives for applying Bayesian methods.
  • To illustrate how these perspectives influence modeling and reporting.
  • To emphasize the heterogeneity of defensible Bayesian practices.

Main Methods:

  • Literature review and conceptual analysis.
  • Identification and description of five Bayesian application perspectives.
  • Discussion of methodological examples within each perspective.

Main Results:

  • Five distinct perspectives on Bayesian methods are presented.
  • Different perspectives lead to varied modeling and reporting recommendations.
  • Practices reasonable under one perspective may be unreasonable under another.

Conclusions:

  • There is significant diversity in defensible Bayesian practices.
  • Understanding these diverse perspectives fosters appreciation for varied orientations in Bayesian analysis.