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The minimum description length principle

Author: Peter D Grünwald
Publisher: Cambridge, Mass. : MIT Press, ©2007.
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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 min.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Grünwald, Peter D.
Minimum description length principle.
Cambridge, Mass. : MIT Press, ©2007
(DLC) 2006046646
(OCoLC)70292149
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Peter D Grünwald
ISBN: 9780262256292 0262256290 1282096354 9781282096356 9781429465601 1429465603
OCLC Number: 123173836
Description: 1 online resource (xxxii, 703 pages) : illustrations
Contents: List of Figures; Series Foreword; Foreword; Preface; PART I --
Introductory Material; 1 --
Learning, Regularity, and Compression; 2 --
Probabilistic and Statistical Preliminaries; 3 --
Information-Theoretic Preliminaries; 4 --
Information-Theoretic Properties of Statistical Models; 5 --
Crude Two-Part Code MDL; PART II --
Universal Coding; 6 --
Universal Coding with Countable Models; 7 --
Parametric Models: Normalized Maximum Likelihood; 8 --
Parametric Models: Bayes; 9 --
Parametric Models: Prequential Plug-in; 10 --
Parametric Models: Two-Part; 11 --
NMLWith Innite Complexity. 12 --
Linear RegressionPART III --
Refined MDL; 14 --
MDL Model Selection; 15 --
MDL Prediction and Estimation; 16 --
MDL Consistency and Convergence; 17 --
MDL in Context; PART IV --
Additional Background; 18 --
The Exponential or "Maximum Entropy" Families; 19 --
Information-Theoretic Properties of Exponential Families; References; List of Symbols; Subject Index.
Series Title: Adaptive computation and machine learning.
Responsibility: Peter D. Grünwald.
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Abstract:

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 min.

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