Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics)
| Author | : | |
| Rating | : | 4.67 (688 Votes) |
| Asin | : | 0521878268 |
| Format Type | : | paperback |
| Number of Pages | : | 656 Pages |
| Publish Date | : | 2015-05-15 |
| Language | : | English |
DESCRIPTION:
Five Stars An excellent book with rigorous proofs
It will be extremely valuable as a textbook for Masters and Ph.D. This book is, without doubt, a must-read for Ph.D. 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 UniversityAdvance praise: 'Bayesian nonparametrics has seen amazing theoretical, methodological, and computational developments in recent years. Fundamentals of Nonparametric Bayesian Inference is the first book to comprehensively cover models, methods, and theories of Bayesian nonparametrics. students and researchers in statistics and probability.' Igor Prünster, Universit Commerciale Luigi Bocconi, MilanAdvance praise: 'Worth waiting for, this book gives a both global and precise overview on the fundamentals of Bayesian nonparametrics. They masterfully cover all major aspects of the discipline, with an emphasis on asym
He is an elected fellow of the Institute of Mathematical Statistics, the American Statistical Association and the International Society for Bayesian Analysis.Aad van der Vaart is Professor of Stochastics at Universiteit Leiden. 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 J
Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.. Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subje