Information and Complexity in Statistical Modeling
Jorma Rissanen - Collection Information Science and Statistics
Résumé
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.
Sommaire
- Shannon
- Wiener information.
- Coding with random processes.
- Universal coding.
- Kolmogorov complexity.
- Stochastic complexity.
- Structure function.
- The MDL principle.
- Applications.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Springer |
| Auteur(s) | Jorma Rissanen |
| Collection | Information Science and Statistics |
| Parution | 13/03/2007 |
| Nb. de pages | 142 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 377g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780387366104 |
| ISBN13 | 978-0-387-36610-4 |
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