Language:

Understanding predictive information criteria for Bayesian models

Statistics and computing, 2014-11, Vol.24 (6), p.997-1016 [Peer Reviewed Journal]

Full text available

Citations Cited by

Title:
Understanding predictive information criteria for Bayesian models
Author: Gelman, Andrew ; Hwang, Jessica ; Vehtari, Aki
Subjects: Artificial Intelligence ; Mathematics and Statistics ; Probability and Statistics in Computer Science ; Statistical Theory and Methods ; Statistics ; Statistics and Computing/Statistics Programs
Is Part Of: Statistics and computing, 2014-11, Vol.24 (6), p.997-1016
Description: 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.
Publisher: Boston: Springer US
Language: English
Identifier: ISSN: 0960-3174
EISSN: 1573-1375
DOI: 10.1007/s11222-013-9416-2
Source: Alma/SFX Local Collection

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

Searching Remote Databases, Please Wait