The Intrinsic Bayes Factor for Model Selection and Prediction
作者:
JamesO. Berger,
LuisR. Pericchi,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 433
页码: 109-122
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476668
出版商: Taylor & Francis Group
关键词: Asymptotic Bayes factors;Hypothesis testing;Noninformative prior;Posterior probability;Training sample
数据来源: Taylor
摘要:
In the Bayesian approach to model selection or hypothesis testing with models or hypotheses of differing dimensions, it is typically not possible to utilize standard noninformative (or default) prior distributions. This has led Bayesians to use conventional proper prior distributions or crude approximations to Bayes factors. In this article we introduce a new criterion called theintrinsic Bayes factor, which is fully automatic in the sense of requiring only standard noninformative priors for its computation and yet seems to correspond to very reasonable actual Bayes factors. The criterion can be used for nested or nonnested models and for multiple model comparison and prediction. From another perspective, the development suggests a general definition of a “reference prior” for model comparison.
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