Advances in Minimum Description Length: Theory and Applications (Hardcover)

Peter D. Grunwald, In Jae Myung, Mark A. Pitt

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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.

Peter D. Grünwald is a researcher at CWI, the National Research Institute for Mathematics and Computer Science, Amsterdam, the Netherlands. He is also affiliated with EURANDOM, the European Research Institute for the Study of Stochastic Phenomena, Eindhoven, the Netherlands.

In Jae Myung is Professor in the Department of Psychology and a member of the Center for Cognitive Science at Ohio State University.

Mark A. Pitt is Professor in the Department of Psychology and a member of the Center for Cognitive Science at Ohio State University.

 

Table of Contents:

Series Foreword vii
Preface ix
I Introductory Chapters 1
1 Introducing the Minimum Description Length Principle
Peter D. Grünwald
3
2 Minimum Description Length Tutorial
Peter D. Grünwald
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 Vitányi
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 Jörnsten and Bin Yu
295
13 An MDL Framework for Data Clustering
Petri Kontkanen, Petri Myllymäki, 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

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歸納推理的過程,即從特定實例中推斷出一般法則和原則,是統計建模、模式識別和機器學習的基礎。最小描述長度(MDL)原則是一種強大的歸納推理方法,它認為,在有限的觀察數據集下,最好的解釋是能夠對數據進行最大壓縮的解釋,即我們能夠對數據進行的壓縮越多,我們對數據底層的規律性就了解得越多。《最小描述長度的進展》是一本將向科學界介紹MDL基礎、最新理論進展和實際應用的參考書。

本書首先對MDL進行了廣泛的教程,涵蓋了其理論基礎、實際影響以及各種解釋和底層哲學。教程包括了MDL的簡要歷史,從科爾莫哥洛夫復雜性的概念到MDL的開始。然後,本書介紹了最近的理論進展,以一種對來自不同科學領域的讀者易於理解的方式介紹了現代MDL方法。本書最後提供了如何在從生物信息學和機器學習到心理學的研究環境中應用MDL的示例。

Peter D. Grünwald是荷蘭阿姆斯特丹國家數學和計算機科學研究所(CWI)的研究員,也是荷蘭愛因多姆歐洲研究所(EURANDOM)的成員,該研究所研究隨機現象。

In Jae Myung是俄亥俄州立大學心理學系的教授,也是認知科學中心的成員。

Mark A. Pitt是俄亥俄州立大學心理學系的教授,也是認知科學中心的成員。


 



目錄:







系列前言
vii




前言
ix



I
入門章節
1



1
介紹最小描述長度原則
Peter D. Grünwald
3<