Buch, Englisch, 142 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g
Buch, Englisch, 142 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g
Reihe: Information Science and Statistics
ISBN: 978-1-4419-2267-0
Verlag: Springer
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Informationstheorie, Kodierungstheorie
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
Weitere Infos & Material
Information and Coding.- Shannon-Wiener Information.- Coding of Random Processes.- Statistical Modeling.- Kolmogorov Complexity.- Stochastic Complexity.- Structure Function.- Optimally Distinguishable Models.- The MDL Principle.- Applications.
