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Advances in minimum description length : theory and applications

著者: Peter D Grünwald; In Jae Myung; Mark A Pitt
出版商: Cambridge, Mass. : MIT Press, ©2005.
丛书: Neural information processing series.
版本/格式:   图书 : 英语查看所有的版本和格式
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材料类型: 互联网资源
文件类型: 书, 互联网资源
所有的著者/提供者: Peter D Grünwald; In Jae Myung; Mark A Pitt
ISBN: 0262072629 9780262072625
OCLC号码: 55960955
描述: x, 444 p. : ill. ; 27 cm.
内容: 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
丛书名: Neural information processing series.
责任: edited by Peter D. Grünwald, In Jae Myung, Mark A. Pitt.
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