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Advances in minimum description length : theory and applications

Author: Peter D Grünwald; In Jae Myung; Mark A Pitt
Publisher: Cambridge, Mass. : MIT Press, ©2005.
Series: Neural information processing series.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Peter D Grünwald; In Jae Myung; Mark A Pitt
ISBN: 0262072629 9780262072625
OCLC Number: 55960955
Description: x, 444 pages : illustrations ; 27 cm.
Contents: 1. Introducing the minimum description length principle / Peter Grunwald --
2. Minimum description length tutorial / Peter Grunwald --
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 Vitanyi --
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.
Series Title: Neural information processing series.
Responsibility: edited by Peter D. Grünwald, In Jae Myung, Mark A. Pitt.
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