The Minimum Description Length Principle (Adaptive Computation and Machine Learning)

Peter D. Grunwald

ISBN 10: 0262072815 / ISBN 13: 9780262072816

Published by Mit Pr, 2007

About this Item

This item is in overall good condition. Covers and dust jackets are intact but may have minor wear including slight curls or bends to corners as well as cosmetic blemishes including stickers. Pages are intact but may have minor highlighting/ writing. Binding is intact; however, spine may have slight wear overall. Digital codes may not be included and have not been tested to be redeemable and/or active. Minor shelf wear overall. Please note that all items are donated goods and are in used condition. Orders shipped Monday through Friday! Your purchase helps put people to work and learn life skills to reach their full potential. Orders shipped Monday through Friday. Your purchase helps put people to work and learn life skills to reach their full potential. Thank you!

Seller Inventory # 466SUS003ML2

Bibliographic Details

• Title: The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
• Publisher: Mit Pr
• Publication Date: 2007
• Language: English
• ISBN 10: 0262072815
• ISBN 13: 9780262072816
• Binding: hardcover
• Condition: Good

About this title

Synopsis

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.

Product Description

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is useful for researchers dealing with inductive reference in diverse areas including statistics, pattern classification, machine learning, data mining, biology, econometrics, and experimental psychology.