Fundamentals of Nonparametric Bayesian Inference

Ghosal, Subhashis/ Van Der Vaart, Aad

ISBN 10: 0521878268 / ISBN 13: 9780521878265

Published by Cambridge Univ Pr, 2017

About this Item

646 pages. 10.00x7.25x1.75 inches. In Stock. This item is printed on demand. Seller Inventory # __0521878268

Bibliographic Details

Title: Fundamentals of Nonparametric Bayesian ...

Publisher: Cambridge Univ Pr

Publication Date: 2017

Binding: Hardcover

Condition: Brand New

About this title

Synopsis:

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 subject areas. 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. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. 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.

Book Description:

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.