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April 2005
8 x 10, 454 pp., 73 illus.
$50.00/£37.95 (CLOTH)
Short

ISBN-10:
0-262-07262-9
ISBN-13:
978-0-262-07262-5

Series
Bradford Books
Neural Information Processing
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Advances in Minimum Description Length
Theory and Applications
Edited by Peter D. Grünwald, In Jae Myung and Mark A. Pitt

Series Forewordvii
Preface
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ix
IIntroductory Chapters1
1Introducing the Minimum Description Length Principle
Peter D. Grünwald
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3
2Minimum Description Length Tutorial
Peter D. Grünwald
23
3MDL, Bayesian Inference, and the Geometry of the Space of Probability Distributions
Vijay Balasubramanian
81
4Hypothesis Testing for Poisson vs. Geometric Distributions Using Stochastic Complexity
Aaron D. Lanterman
99
5Applications of MDL to Selected Families of Models
Andrew J. Hanson and Philip Chi-Wing Fu
125
6Algorithmic Statistics and Kolmogorov's Structure Functions
Paul Vitányi
151
IITheoretical Advances175
7Exact Minimax Predictive Density Estimation and MDL
Feng Liang and Andrew Barron
177
8The Contribution of Parameters to Stochastic Complexity
Dean P. Foster and Robert A. Stine
195
9Extended Stochastic Complexity and Its Applications to Learning
Kenji Yamanishi
215
10Kolmogorov's Structure Function in MDL Theory and Lossy Data Compression
Jorma Rissanen and Ioan Tabus
245
IIIPractical Applications263
11Minimum Message Length and Generalized Bayesian Nets with Asymmetric Languages
Joshua W. Comley and David L. Dowe
265
12Simultaneous Clustering and Subset Selection via MDL
Rebecka Jörnsten and Bin Yu
295
13An MDL Framework for Data Clustering
Petri Kontkanen, Petri Myllymäki, Wray Buntine, Jorma Rissanen and Henry Tirri
323
14Minimum Description Length and Psychological Clustering Models
Michael D. Lee and Daniel J. Navarro
355
15A Minimum Description Length Principle for Perception
Nick Chater
385
16Minimum Description Length and Cognitive Modeling
Yong Su, In Jae Myung and Mark A. Pitt
411
Index
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435
 
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