Subhashis Ghosal (North Carolina State University), Aad van der Vaart (Universiteit Leiden)

Fundamentals of Nonparametric Bayesian Inference

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Cambridge Series in Statistical and Probabilistic Mathematics
• Verlag: Cambridge University Press
• Seitenzahl: 672
• Erscheinungstermin: 19. März 2019
• Englisch
• Abmessung: 260mm x 183mm x 40mm
• Gewicht: 1370g
• ISBN-13: 9780521878265
• ISBN-10: 0521878268
• Artikelnr.: 47350216

Autorenporträt

Subhashis Ghosal is Professor of Statistics at North Carolina State University. His primary research interest is in the theory, methodology and various applications of Bayesian nonparametrics. He has edited one book, written nearly one hundred papers, and serves on the editorial boards of the Annals of Statistics, Bernoulli, and the Electronic Journal of Statistics. He is an elected fellow of the Institute of Mathematical Statistics, the American Statistical Association and the International Society for Bayesian Analysis.

Inhaltsangabe

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.