Bayesian Nonparametrics

by Jayanta K. Ghosh, Paul R. Rosenbaum, R.V. Ramamoorthi

This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.

320 pages, Hardcover

First published January 1, 2003

Reviews

[Nils Lid Hjort · Rating 4 out of 5]

For some reason Goodreads lists *three authors* for this book, but it's really merely two: JK Ghosh and Ramamoorthi.

I've been working with both (and am given a bit of credit in their preface), and have read this book, though perhaps not from A to Å. It's pretty mathematical in its setup and style of story telling, so to speak, without so much efforts regarding the statistical motivation for the various prior constructions. They are rigorous, not shying away from all required details.

I'm not satisfied with Springer here -- the authors have produced a pdf, which the publisher has printer verbatim, without any efforts towards making it nicer in outlook and design. If a copy editor had been given 24 ours, with the LaTeX files, it could have been significantly improved.