Watanabe–Akaike information criterion
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In statistics, the widely applicable information criterion (WAIC), also known as Watanabe–Akaike information criterion, is the generalized version of the Akaike information criterion (AIC) onto singular statistical models.[1]
Widely applicable Bayesian information criterion (WBIC) is the generalized version of Bayesian information criterion (BIC) onto singular statistical models.[2]
WBIC is the average log likelihood function over the posterior distribution with the inverse temperature > 1/log n where n is the sample size.[2]
Both WAIC and WBIC can be numerically calculated without any information about a true distribution.
See also[edit]
- Akaike information criterion
- Bayesian information criterion
- Deviance information criterion
- Hannan–Quinn information criterion
References[edit]
- ^ Watanabe, Sumio (2010). "Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory". Journal of Machine Learning Research. 11: 3571–3594.
- ^ a b Watanabe, Sumio (2013). "A Widely Applicable Bayesian Information Criterion" (PDF). Journal of Machine Learning Research. 14: 867–897.
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