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arXiv · 1307.5928

Understanding predictive information criteria for Bayesian models

Andrew Gelman·Jessica Hwang·Aki Vehtari
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Abstract

We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a biascorrected adjustment of within-sample error. We focus on the choices involved in setting up these measures, and we compare them in three simple examples, one theoretical and two applied. The contribution of this review is to put all these information criteria into a Bayesian predictive context and to better understand, through small examples, how these methods can apply in practice.

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Andrew Gelman, Jessica Hwang, Aki Vehtari. 2013-07-23. Understanding predictive information criteria for Bayesian models. https://arxiv.org/abs/1307.5928

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