Information and Complexity in Statistical Modeling
(Sprache: Englisch)
The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The prerequisites include basic probability calculus and statistics.
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Produktinformationen zu „Information and Complexity in Statistical Modeling “
The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The prerequisites include basic probability calculus and statistics.
Klappentext zu „Information and Complexity in Statistical Modeling “
No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
Inhaltsverzeichnis zu „Information and Complexity in Statistical Modeling “
- Introduction- Shannon-Wiener information
- Coding with random processes
- Universal coding
- Kolmogorov complexity
- Stochastic complexity
- Structure function
- The MDL principle
- Applications
Bibliographische Angaben
- Autor: Jorma Rissanen
- 2007, 142 Seiten, Maße: 16,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer, New York
- ISBN-10: 0387366105
- ISBN-13: 9780387366104
Sprache:
Englisch
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