Mathematical Theory of Bayesian Statistics
«
"Information criteria are introduced from the two viewpoints, model selection and hyperparameter optimization. In each viewpoint, the properties of the generalization loss and the free energy or the minus log marginal likelihood are investigated. The book is very nicely written with well-defined concepts and contexts. I recommend to all students and researchers." ~Rozsa Horvath-Bokor, Zentralblatt MATH
»
Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Les mer
Features
Explains Bayesian inference not subjectively but objectively.
Provides a mathematical framework for conventional Bayesian theorems.
Introduces and proves new theorems.
Cross validation and information criteria of Bayesian statistics are studied from the mathematical point of view.
Illustrates applications to several statistical problems, for example, model selection, hyperparameter optimization, and hypothesis tests.
This book provides basic introductions for students, researchers, and users of Bayesian statistics, as well as applied mathematicians.
Author
Sumio Watanabe is a professor of Department of Mathematical and Computing Science at Tokyo Institute of Technology. He studies the relationship between algebraic geometry and mathematical statistics.
Detaljer
- Forlag
- Chapman & Hall/CRC
- Innbinding
- Paperback
- Språk
- Engelsk
- Sider
- 332
- ISBN
- 9780367734817
- Utgivelsesår
- 2020
- Format
- 23 x 16 cm
Anmeldelser
«
"Information criteria are introduced from the two viewpoints, model selection and hyperparameter optimization. In each viewpoint, the properties of the generalization loss and the free energy or the minus log marginal likelihood are investigated. The book is very nicely written with well-defined concepts and contexts. I recommend to all students and researchers." ~Rozsa Horvath-Bokor, Zentralblatt MATH
»