Advances in Minimum Description Length

Theory and Applications

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

450 pages, parution le 04/10/2005

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Résumé

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.

L'auteur - Peter D. Grünwald

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.

L'auteur - In Jae Myung

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

L'auteur - Mark A. Pitt

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

Sommaire

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

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Caractéristiques techniques

	PAPIER
Éditeur(s)	The MIT Press
Auteur(s)	Peter D. Grünwald, In Jae Myung, Mark A. Pitt
Parution	04/10/2005
Nb. de pages	450
Format	20,5 x 26
Couverture	Relié
Poids	1125g
Intérieur	Noir et Blanc
EAN13	9780262072625
ISBN13	978-0-262-07262-5