 Watanabe–Akaike information criterion

The Watanabe–Akaike Information Criterion (WAIC) is a model selection criterion used in statistics, particularly for assessing the fit of Bayesian models. It is an extension of the Akaike Information Criterion (AIC) and is designed to handle situations where there are complex models, especially in the context of Bayesian inference.