Eric-Jan Wagenmakers, Peter Grünwald, Mark Steyvers
This article reviews the rationale for using accumulative one-step-ahead prediction error (APE) as a data-driven method for model selection. Theoretically, APE is closely related to Bayesian model selection and the method of minimum description length (MDL). The sole requirement for using APE is that the models under consideration are capable of generating a prediction for the next, unseen data point. This means that APE may be readily applied to selection problems involving very complex models. APE automatic…
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