Bayesian Nonparametric Data Analysis
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the
- PDF / 5,187,987 Bytes
- 203 Pages / 439.42 x 683.15 pts Page_size
- 20 Downloads / 322 Views
Peter Müller Fernando Andrés Quintana Alejandro Jara Tim Hanson
Bayesian Nonparametric Data Analysis
Springer Series in Statistics
Series Editors Peter Bickel, CA, USA Peter Diggle, Lancaster, UK Stephen E. Fienberg, Pittsburgh, PA, USA Ursula Gather, Dortmund, Germany Ingram Olkin, Stanford, CA, USA Scott Zeger, Baltimore, MD, USA
More information about this series at http://www.springer.com/series/692
Peter Müller • Fernando Andrés Quintana • Alejandro Jara • Tim Hanson
Bayesian Nonparametric Data Analysis
123
Peter Müller Department of Mathematics University of Texas at Austin Austin, TX USA
Fernando Andrés Quintana Departamento de Estadística Pontificia Universidad Católica Santiago, Chile
Alejandro Jara Departamento de Estadística Pontificia Universidad Católica Santiago, Chile
Tim Hanson Department of Statistics University of South Carolina Columbia, SC USA
ISSN 0172-7397 Springer Series in Statistics ISBN 978-3-319-18967-3 DOI 10.1007/978-3-319-18968-0
ISSN 2197-568X
(electronic)
ISBN 978-3-319-18968-0
(eBook)
Library of Congress Control Number: 2015943065 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
In this book, we review nonparametric Bayesian methods and models. The organization of the book follows a data analysis perspective. Rather than focusing on specific models, chapters are organized by traditional data analysis problems. For each problem, we introduce suitable nonparametric Bayesian models and show how they are used to implement inference in the given data analysis problem. In selecting specific nonparametric models, we favor simpler and traditional models over specialized ones. The organization by inferential problem leads to some repetition in the discussion of specific models when t
Data Loading...
