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Understanding predictive information criteria for Bayesian models

arXiv 1307.5928 ↗First posted 23 Jul 2013stat.MEcited by 51

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Abstract (arXiv)

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.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2013.

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Citing papers per year: 2021: 5; 2022: 12; 2023: 9; 2024: 6; 2025: 10; 2026: 9.

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