How New Information Criteria WAIC and WBIC Worked for MLP Model Selection

The present paper evaluates newly invented information criteria for singular models. Well-known criteria such as AIC and BIC are valid for regular statistical models, but their validness for singular models is not guaranteed. Statistical models such as multilayer perceptrons (MLPs), RBFs, HMMs are singular models. Recently WAIC and WBIC have been proposed as new information criteria for singular models. They are developed on a strict mathematical basis, and need empirical evaluation. This paper experimentally evaluates how WAIC and WBIC work for MLP model selection using conventional and new learning methods.