A simple application of FIC to model selection

Although the predictivity of a model is a central objective in model building in science, it is only one of a wide range of criteria considered. We also seek models that are motivated by our understanding of the underlying mechanisms that give rise to phenomena and the idea of model parsimony is often a useful guiding principle, especially in physics. In contrast to this broad view of model selection, this paper describes the application of a theory for model selection motivated and entirely justified by a narrow definition of model predictivity: the ability of a model to predict a new observation generated by a stochastic process, after the model parameters have been fit to a finite number of previous observations.