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ADVANCES IN MINIMUM DESCRIPTION LENGTH 2005

△看放大圖
ISBN: 0262072629
類別: 企業概論/溝通Business
出版社: MIT PRESS
作者: GRUNWALD
年份: 2005
裝訂別: 精裝
頁數: 372
定價: 1,550
售價: 1,395
原幣價: USD 50.00
狀態: 缺書
The process of inductive inference -- to infer general laws and principles from particular instances -- is the basis of statistical modeling, pattern recognition, and machine learning. The Minimum Descriptive Length (MDL) principle, a powerful method of inductive inference, holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data -- that the more we are able to compress the data, the more we learn about the regularities underlying the data. Advances in Minimum Description Length is a sourcebook that will introduce the scientific community to the foundations of MDL, recent theoretical advances, and practical applications.

The book begins with an extensive tutorial on MDL, covering its theoretical underpinnings, practical implications as well as its various interpretations, and its underlying philosophy. The tutorial includes a brief history of MDL -- from its roots in the notion of Kolmogorov complexity to the beginning of MDL proper. The book then presents recent theoretical advances, introducing modern MDL methods in a way that is accessible to readers from many different scientific fields. The book concludes with examples of how to apply MDL in research settings that range from bioinformatics and machine learning to psychology.

Table of Contents

Series Foreword vii

Preface ix

I Introductory Chapters 1

1 Introducing the Minimum Description Length Principle
Peter D. Grunwald 3

2 Minimum Description Length Tutorial
Peter D. Grunwald 23

3 MDL, Bayesian Inference, and the Geometry of the Space of Probability Distributions
Vijay Balasubramanian 81

4 Hypothesis Testing for Poisson vs. Geometric Distributions Using Stochastic Complexity
Aaron D. Lanterman 99

5 Applications of MDL to Selected Families of Models
Andrew J. Hanson and Philip Chi-Wing Fu 125

6 Algorithmic Statistics and Kolmogorov's Structure Functions
Paul Vitanyi 151

II Theoretical Advances 175

7 Exact Minimax Predictive Density Estimation and MDL
Feng Liang and Andrew Barron 177

8 The Contribution of Parameters to Stochastic Complexity
Dean P. Foster and Robert A. Stine 195

9 Extended Stochastic Complexity and Its Applications to Learning
Kenji Yamanishi 215

10 Kolmogorov's Structure Function in MDL Theory and Lossy Data Compression
Jorma Rissanen and Ioan Tabus 245

III Practical Applications 263

11 Minimum Message Length and Generalized Bayesian Nets with Asymmetric Languages
Joshua W. Comley and David L. Dowe 265

12 Simultaneous Clustering and Subset Selection via MDL
Rebecka Jornsten and Bin Yu 295

13 An MDL Framework for Data Clustering
Petri Kontkanen, Petri Myllymaki, Wray Buntine, Jorma Rissanen and Henry Tirri 323

14 Minimum Description Length and Psychological Clustering Models
Michael D. Lee and Daniel J. Navarro 355

15 A Minimum Description Length Principle for Perception
Nick Chater 385

16 Minimum Description Length and Cognitive Modeling
Yong Su, In Jae Myung and Mark A. Pitt 411

Index 435
Springer 國外現貨
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