Description: 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.
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EAN: 9780521878265
UPC: 9780521878265
ISBN: 9780521878265
MPN: N/A
Item Length: 26.5 cm
Number of Pages: 670 Pages
Language: English
Publication Name: Fundamentals of Nonparametric Bayesian Inference
Publisher: Cambridge University Press
Publication Year: 2017
Subject: Mathematics
Item Height: 260 mm
Item Weight: 1360 g
Type: Textbook
Author: Subhashis Ghosal, Aad Van Der Vaart
Item Width: 182 mm
Format: Hardcover
