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The minimum description length principle

Author: Peter D Grünwald
Publisher: Cambridge, Mass. : MIT Press, ©2007.
Series: Adaptive computation and machine learning.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.
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Details

Genre/Form: Online-Publikation
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Peter D Grünwald
ISBN: 0262072815 9780262072816
OCLC Number: 70292149
Description: xxxii, 703 pages : illustrations ; 24 cm
Contents: 1. Learning, regularity, and compression --
2. Probabilistic and statistical preliminaries --
3. Information-theoretic preliminaries --
4. Information-theoretic properties of statistical models --
5. Crude two-part code MDL --
6. Universal coding with countable models --
7. Parametric models : normalized maximum likelihood --
8. Parametric models : Bayes --
9. Parametric models : prequential plug-in --
10. Parametric models : two-part --
11. NML with infinite complexity --
12. Linear regression --
13. Beyond parametrics --
14. MDL model selection --
15. MDL prediction and estimation --
16. MDL consistency and convergence --
17. MDL in context --
18. The exponential or "maximum entropy" families --
19. Information-theoretic properties of exponential families.
Series Title: Adaptive computation and machine learning.
Responsibility: Peter D. Grünwald.

Abstract:

This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.

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