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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: Cambridge, Mass. : MIT Press, ©2005.
シリーズ: Neural information processing series.
エディション/フォーマット:   book_printbook : Englishすべてのエディションとフォーマットを見る
データベース:WorldCat
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資料の種類: インターネット資料
ドキュメントの種類: 図書, インターネットリソース
すべての著者/寄与者: Peter D Grünwald; In Jae Myung; Mark A Pitt
ISBN: 0262072629 9780262072625
OCLC No.: 55960955
物理形態: x, 444 pages : illustrations ; 27 cm.
コンテンツ: 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.
シリーズタイトル: Neural information processing series.
責任者: edited by Peter D. Grünwald, In Jae Myung, Mark A. Pitt.
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