The Minimum Description Length Principle

The minimum description length (MDL) principle is a powerful method of inductive inference the basis of statistical modeling pattern recognition and machine learning. It holds that the best explanation given a limited set of observed data is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection prediction and estimation problems in situations where the models under consideration can be arbitrarily complex and overfitt...See More