The minimum description length principle / Peter D. Grünwald.

Format:

Book

Author/Creator:

Grünwald, Peter D.

Series:

Adaptive computation and machine learning.

Adaptive computation and machine learning

Language:

English

Subjects (All):

Minimum description length (Information theory).

Physical Description:

1 online resource (736 p.)

Edition:

1st ed.

Place of Publication:

Cambridge, Mass. : MIT Press, c2007.

Language Note:

English

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.

Includes bibliographical references (p. [651]-673) and indexes.

Made available online by EBSCO.

OCLC-licensed vendor bibliographic record.

ISBN:

1-282-09635-4

9786612096358

0-262-25629-0

1-4294-6560-3

OCLC:

123173836