The Minimum Description Length (MDL) principle is an information theoretic approach to
inductive inference that originated in algorithmic coding theory. In this approach, data are
viewed as codes to be compressed by the model. From this perspective, models are
compared on their ability to compress a data set by extracting useful information in the data
apart from random noise. The goal of model selection is to identify the model, from a set of
candidate models, that permits the shortest description length (code) of the data. Since …