  Model or variable selection is usually achieved through ranking models
according to the increasing order of preference. One of methods is applying
Kullback-Leibler distance or relative entropy as a selection criterion. Yet
that will raise two questions, why uses this criterion and are there any other
criteria. Besides, conventional approaches require a reference prior, which is
usually difficult to get. Following the logic of inductive inference proposed
by Caticha, we show relative entropy to be a unique criterion, which requires
no prior information and can be applied to different fields. We examine this
criterion by considering a physical problem, simple fluids, and results are
promising.
