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Advances in minimum description length : theory and applications

저자: Peter D Grünwald; In Jae Myung; Mark A Pitt
출판사: Cambridge, Mass. : MIT Press, ©2005.
시리즈: Neural information processing series.
판/형식:   Print book : 영어모든 판과 형식 보기
데이터베이스:WorldCat
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자료 유형: 인터넷 자료
문서 형식: 책, 인터넷 자원
모든 저자 / 참여자: Peter D Grünwald; In Jae Myung; Mark A Pitt
ISBN: 0262072629 9780262072625
OCLC 번호: 55960955
설명: x, 444 pages : illustrations ; 27 cm.
내용: 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.
일련 제목: Neural information processing series.
책임: edited by Peter D. Grünwald, In Jae Myung, Mark A. Pitt.
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