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Understanding predictive information criteria for Bayesian models. (English) Zbl 1332.62090
Summary: 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 bias-corrected 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 paper 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.

##### MSC:
 62F15 Bayesian inference 62B10 Statistical aspects of information-theoretic topics
##### Keywords:
AIC; DIC; WAIC; cross-validation; prediction; Bayes
##### Software:
BUGS; BayesDA; bootstrap
Full Text:
##### References:
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