The minimum description length principle
Peter D. Grünwald (Author)
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
Online-Publikation
xxxii, 703 pages : illustrations ; 24 cm
9780262072816, 9780262529631, 0262072815, 0262529637
70292149
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
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