Abstract

This chapter differs from later chapters in scope, because Bayesian analysis is an essentially self-contained paradigm for statistics. (Later chapters will, for the most part, deal with special topics within frequentist decision theory.) In order to provide a satisfactory perspective on Bayesian analysis, we will discuss Bayesian inference along with Bayesian decision theory. Before beginning the study, however, we briefly discuss the seven major arguments that can be given in support of Bayesian analysis. (Later chapters will similarly begin with a discussion of justifications.) Some of these arguments will not be completely understandable initially, but are best placed together for reference purposes.

Keywords

Posterior Distribution Bayesian Analysis Prior Density Posterior Odds Ratio Bayesian Robustness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1985

Authors and Affiliations

  • James O. Berger
    • 1
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurhamUSA

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