Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics

  • Gelman A
  • 63

    Readers

    Mendeley users who have this article in their library.
  • 17

    Citations

    Citations of this article.

Abstract

Bayes, Jeffreys, Prior Distributions and the Philosophy of Statistics 1 Andrew Gelman 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, Jef-freys 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, how-ever, feel that it is important to understand where our probability models come from, and I welcome the op-portunity to use the present article by Robert, Chopin and Rousseau as a platform for further discussion of foundational issues. 2 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 sim-plicity; and (3) a key generalization of Jeffreys's ideas is to explicitly include model checking in the process of data analysis.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Andrew Gelman

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free