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. It is used as measure of how well the model will predict data it wasn't trained on.