Home

Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics

Andrew E. Gelman

Title:
Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics
Author(s):
Gelman, Andrew E.
Date:
Type:
Articles
Department:
Statistics
Volume:
24
Permanent URL:
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
Publisher DOI:
10.1214/09-STS284D
Item views:
225
Metadata:
View

In Partnership with the Center for Digital Research and Scholarship at Columbia University Libraries/Information Services.