Watanabe–Akaike information criterion
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.
Both WAIC and WBIC can be numerically calculated without any information about a true distribution.
- 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.
- Watanabe, Sumio (2013). "A Widely Applicable Bayesian Information Criterion" (PDF). Journal of Machine Learning Research. 14: 867–897.
|This statistics-related article is a stub. You can help Wikipedia by expanding it.|