Watanabe-Akaike or Widely Available Information Criterion (WAIC)

Computes Watanabe-Akaike or Widely Available Information Criterion (WAIC), an adjusted within-sample measure of predictive accuracy, for models estimated using Bayesian methods.

Usage

WAIC(...)

Arguments

...

one or more fitted model objects of the same class.

Value

A numeric matrix containing the WAIC values corresponding to each fitted object supplied in ....

References

Watanabe S. (2010). Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. The Journal of Machine Learning Research, 11, 3571–3594.