The Minimum Description Length Principle - Table of Contents

June 2007
8 x 10, 504 pp., 12 illus.
$47.00/£34.95 (CLOTH)
Short

ISBN-10:
0-262-07281-5
ISBN-13:
978-0-262-07281-6

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The Minimum Description Length Principle
Peter D. Grünwald
Foreword by Jorma Rissanen

	List of Figures	xix

	Series Foreword	xxi

	Foreword	xxiii

	Preface Sample Chapter - Download PDF (86 KB)	xxv

I	Introductory Material	1

1	Learning, Regularity, and Compression Sample Chapter - Download PDF (370 KB)	3

2	Probabilistic and Statistical Preliminaries	41

3	Information-Theoretic Preliminaries	79

4	Information-Theoretic Properties of Statistical Models	109

5	Crude Two-Part Code MDL	131

II	Universal Coding	165

6	Universal Coding with Countable Models	171

7	Parametric Models: Normalized Maximum Likelihood	207

8	Parametric Models: Bayes	231

9	Parametric Models: Prequential Plug-in	257

10	Parametric Models: Two-Part	271

11	NML With Infinite Complexity	295

12	Linear Regression	335

13	Beyond Parametrics	369

III	Refined MDL	403

14	MDL Model Selection	409

15	MDL Prediction and Estimation	459

16	MDL Consistency and Convergence	501

17	MDL in Context	523

IV	Additional Background	597

18	The Exponential or "Maximum Entropy" Families	599

19	Information-Theoretic Properties of Exponential Families	623

	References	651

	List of Symbols	675

	Subject Index Sample Chapter - Download PDF (142 KB)	679

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