## Bayes factors and model uncertainty (1993)

Venue: | DEPARTMENT OF STATISTICS, UNIVERSITY OFWASHINGTON |

Citations: | 89 - 6 self |

### BibTeX

@TECHREPORT{Kass93bayesfactors,

author = {Robert E. Kass and Adrian E. Raftery},

title = {Bayes factors and model uncertainty},

institution = {DEPARTMENT OF STATISTICS, UNIVERSITY OFWASHINGTON},

year = {1993}

}

### Years of Citing Articles

### OpenURL

### Abstract

In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is one-half. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P-values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this paper we review and discuss the uses of Bayes factors in the context of five scientific applications. The points we emphasize are:- from Jeffreys's Bayesian point of view, the purpose of hypothesis testing is to evaluate the evidence in favor of a scientific theory;- Bayes factors offer a way of evaluating evidence in favor ofa null hypothesis;- Bayes factors provide a way of incorporating external information into the evaluation of evidence about a hypothesis;- Bayes factors are very general, and do not require alternative models to be nested;- several techniques are available for computing Bayes factors, including asymptotic approximations which are easy to compute using the output from standard packages that maximize likelihoods;- in "non-standard " statistical models that do not satisfy common regularity conditions, it can be technically simpler to calculate Bayes factors than to derive non-Bayesian significance