A comprehensive introduction and reference guide to the minimum
description length (MDL) Principle that is accessible to
researchers dealing with inductive reference in diverse areas
including statistics, pattern classification, machine learning,
data mining, biology, econometrics, and experimental psychology, as
well as philosophers interested in the foundations of statistics.
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 overfitting the
data is a serious concern. This extensive, step-by-step
introduction to the MDL Principle provides a comprehensive
reference (with an emphasis on conceptual issues) that is
accessible to graduate students and researchers in statistics,
pattern classification, machine learning, and data mining, to
philosophers interested in the foundations of statistics, and to
researchers in other applied sciences that involve model selection,
including biology, econometrics, and experimental psychology. Part
I provides a basic introduction to MDL and an overview of the
concepts in statistics and information theory needed to understand
MDL. Part II treats universal coding, the information-theoretic
notion on which MDL is built, and part III gives a formal treatment
of MDL theory as a theory of inductive inference based on universal
coding. Part IV provides a comprehensive overview of the
statistical theory of exponential families with an emphasis on
their information-theoretic properties. The text includes a number
of summaries, paragraphs offering the reader a "fast track" through
the material, and boxes highlighting the most important concepts.
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