Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 44)

Ghosal, Subhashis; van der Vaart, Aad

ISBN 10: 0521878268 / ISBN 13: 9780521878265

Edité par Cambridge University Press, 2017

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Hardback book in nearly new condition: firm and square with strong joints. Just a few hardly noticeable rubs; hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so a very nice book in great condition, now offered for sale at a reasonable price. N° de réf. du vendeur 203158

Détails bibliographiques

Titre : Fundamentals of Nonparametric Bayesian ...

Éditeur : Cambridge University Press

Date d'édition : 2017

Reliure : Hardcover

Etat : Like New

Edition : First Edition.

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Revue de presse:

'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

'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 Prünster, Università Commerciale Luigi Bocconi, Milan

'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, Université Paris-Dauphine

'This book can serve as a textbook for a graduate course on Bayesian nonparametrics. It can also be used as a reference book for researchers in both statistics and machine learning, as well as application areas such as econometrics and biostatistics.' Yuehua Wu, MathSciNet