| 词条 | Watanabe–Akaike information criterion |
| 释义 |
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. References1. ^{{cite journal |authorlink=Sumio Watanabe |first=Sumio |last=Watanabe |year=2010 |title=Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory |journal=Journal of Machine Learning Research |volume=11 |pages=3571–3594 }} {{DEFAULTSORT:Watanabe-Akaike information criterion}}{{statistics-stub}}2. ^1 {{cite journal |first=Sumio |last=Watanabe |year=2013 |url=http://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf |title=A Widely Applicable Bayesian Information Criterion |journal=Journal of Machine Learning Research |volume=14 |pages=867–897 }} 2 : Model selection|Bayesian statistics |
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