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Machine learning / Bayesian statistics / Regression analysis / Deviance information criterion / Maximum likelihood / Cross-validation / Fisher information / Akaike information criterion / Bayesian information criterion / Statistics / Estimation theory / Model selection


Understanding predictive information criteria for Bayesian models∗ Andrew Gelman†, Jessica Hwang‡, and Aki Vehtari§ 14 Aug 2013 Abstract We review the Akaike, deviance, and Watanabe-Akaike information criteria fro
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Document Date: 2013-08-14 15:26:41

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City

Espoo / Cambridge / /

Company

Draper / Linde / /

Country

Finland / /

Facility

Institute of Education Sciences / Columbia University / Aalto University / Harvard University / /

Organization

Institute of Education Sciences / Department of Biomedical Engineering and Computational Science / Harvard University / Aalto University / National Science Foundation / Academy of Finland / Columbia University / New York / Department of Statistics / /

Person

Aki Vehtari / Andrew Gelman / Jessica Hwang / /

Position

Fisher / representative / single data model / the Fisher information matrix / /

ProvinceOrState

New York / Massachusetts / /

Technology

machine learning / simulation / /

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