Franklin

The minimum description length principle [electronic resource] / Peter D. Gr?unwald.

Other records:
Author/Creator:
Gr?unwald, Peter D.
Publication:
Cambridge, Mass. : MIT Press, c2007.
Series:
Adaptive computation and machine learning.
Adaptive computation and machine learning
Format/Description:
Book
1 online resource (736 p.)
Subjects:
Minimum description length (Information theory).
Form/Genre:
Electronic books.
Language:
English
System Details:
Mode of access: World Wide Web
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.
Contents:
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
Notes:
Description based upon print version of record.
Made available online by EBSCO.
Includes bibliographical references (p. [651]-673) and indexes.
ISBN:
1-282-09635-4
9786612096358
0-262-25629-0
1-4294-6560-3
OCLC:
123173836
Access Restriction:
Restricted to subscribers or individual electronic text purchasers.
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