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Fundamentals of Nonparametric Bayesian Inference by Subhashis Ghosal: Used
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- Buy It NowFundamentals of Nonparametric Bayesian Inference by Subhashis Ghosal: Used
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eBay item number:364429859772
Item specifics
- Condition
- Book Title
- Fundamentals of Nonparametric Bayesian Inference
- Publication Date
- 2017-06-26
- ISBN
- 9780521878265
- Publication Name
- Fundamentals of Nonparametric Bayesian Inference
- Item Length
- 10.2in
- Publisher
- Cambridge University Press
- Series
- Cambridge Series in Statistical and Probabilistic Mathematics Ser.
- Publication Year
- 2017
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.6in
- Item Width
- 7.2in
- Item Weight
- 48.1 Oz
- Number of Pages
- 670 Pages
About this product
Product Information
Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
0521878268
ISBN-13
9780521878265
eBay Product ID (ePID)
229538744
Product Key Features
Publication Name
Fundamentals of Nonparametric Bayesian Inference
Format
Hardcover
Language
English
Series
Cambridge Series in Statistical and Probabilistic Mathematics Ser.
Publication Year
2017
Type
Textbook
Number of Pages
670 Pages
Dimensions
Item Length
10.2in
Item Height
1.6in
Item Width
7.2in
Item Weight
48.1 Oz
Additional Product Features
Series Volume Number
Series Number 44
Lc Classification Number
Qa278.8.G46 2016
Reviews
Advance praise: 'Bayesian nonparametrics has seen amazing theoretical, methodological, and computational developments in recent years. This timely book gives an authoritative account of the current state of the art by two leading scholars in the field. They masterfully cover all major aspects of the discipline, with an emphasis on asymptotics, and achieve the rare feat of being simultaneously broad and deep, while preserving the utmost mathematical rigor. This book is, without doubt, a must-read for Ph.D. students and researchers in statistics and probability.' Igor Prnster, Universit... Commerciale Luigi Bocconi, Milan, Advance praise: 'Worth waiting for, this book gives a both global and precise overview on the fundamentals of Bayesian nonparametrics. It will be extremely valuable as a textbook for Masters and Ph.D. students, along with more experienced researchers, as the authors have managed to gather, link together, and present with great clarity a large part of the major advances in Bayesian nonparametric modeling and theory.' Judith Rousseau, Universit Paris-Dauphine, Advance praise: 'Probabilistic inference of massive and complex data has received much attention in statistics and machine learning, and Bayesian nonparametrics is one of the core tools. Fundamentals of Nonparametric Bayesian Inference is the first book to comprehensively cover models, methods, and theories of Bayesian nonparametrics. Readers can learn basic ideas and intuitions as well as rigorous treatments of underlying theories and computations from this wonderful book.' Yongdai Kim, Seoul National University
Table of Content
Preface; Glossary of symbols; 1. Introduction; 2. Priors on function spaces; 3. Priors on spaces of probability measures; 4. Dirichlet processes; 5. Dirichlet process mixtures; 6. Consistency: general theory; 7. Consistency: examples; 8. Contraction rates: general theory; 9. Contraction rates: examples; 10. Adaptation and model selection; 11. Gaussian process priors; 12. Infinite-dimensional Bernstein-von Mises theorem; 13. Survival analysis; 14. Discrete random structures; Appendices; References; Author index; Subject index.
Copyright Date
2016
Topic
Probability & Statistics / General, Probability & Statistics / Bayesian Analysis
Lccn
2016-056429
Dewey Decimal
519.542
Intended Audience
Scholarly & Professional
Dewey Edition
23
Illustrated
Yes
Genre
Mathematics
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