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Discussion by Mike West

  • Eric D. Feigelson
  • G. Jogesh Babu

Abstract

It is no surprise (to a Bayesian) that inferential problems encountered using traditional statistical methods have a tendency to evaporate when the issues are addressed from formal Bayesian perspectives. This paper is largely concerned with cameo examples designed to communicate this precept to astronomers interested and involved in statistical work in their investigations. I like the paper and have a great deal of respect for the entrepreneurial efforts of the author and his co-authors to shift the focus of statistical analysis in the field toward the Bayesian paradigm. That these efforts have been rewarded is clear from browsing some of the referenced articles, notably the works of Loredo and Lamb (referenced in text). Here we find advanced physical and statistical models subjected to formal and rigorous Bayesian analysis, some requiring high dimensional numerical integrations performed via Monte Carlo, that yield inferences in terms of posterior distributions for parameters of interest that clearly and unambiguously address detailed and substantive scientific issues. A reading of these works provides a clear picture of investigators led to adherence to the Bayesian paradigm on pragmatic and empirical grounds — the Bayesian approach gets the (right) job done where all others fail.

Keywords

Bayesian Inference Bayesian Computation Statistical Decision Theory Traditional Statistical Method Inferential Problem 
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-Verlag New York, Inc. 1992

Authors and Affiliations

  • Eric D. Feigelson
    • 1
  • G. Jogesh Babu
    • 2
  1. 1.Department of Astronomy and AstrophysicsPennsylvania State UniversityUSA
  2. 2.Department of StatisticsPennsylvania State UniversityUSA

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