Title:

Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics

Author(s):

Gelman, Andrew E.

Date:

2009

Type:

Articles

Department:

Statistics

Volume:

24

Permanent URL:

http://hdl.handle.net/10022/AC:P:8563

Book/Journal Title:

Statistical Science

Abstract:

I actually own a copy of Harold Jeffreys's Theory of Probability but have only read small bits of it, most recently over a decade ago to confirm that, indeed, Jeffreys was not too proud to use a classical chi-squared p-value when he wanted to check the misfit of a model to data (Gelman, Meng and Stern, 2006). I do, however, feel that it is important to understand where our probability models come from, and I welcome the opportunity to use the present article by Robert, Chopin and Rousseau as a platform for further discussion of foundational issues. In this brief discussion I will argue the following: (1) in thinking about prior distributions, we should go beyond Jeffreys's principles and move toward weakly informative priors; (2) it is natural for those of us who work in social and computational sciences to favor complex models, contra Jeffreys's preference for simplicity; and (3) a key generalization of Jeffreys's ideas is to explicitly include model checking in the process of data analysis.

Subject(s):

Statistics

DOI:

10.1214/09-STS284D

Item views:

197

Metadata:

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