Overview
- This is the first text to introduce nonparametric Bayesian inference from a data analysis perspective
- Includes a large number of examples to illustrate the application of nonparametric Bayesian models for important statistical inference Problems
- Features an extensive discussion of computational details for a practical implementation, including R code for many of the examples
Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (9 chapters)
Keywords
About this book
The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Reviews
“There is much to like about the book under review. The authors present Bayesian nonparametric statistics focusing on how it is applied in data analysis. … This is a book for a statistician or graduate student that has accepted the Bayesian approach and would like to know more about Bayesian approaches to nonparametric problems.” (Ross S. McVinish, Mathematical Reviews, February, 2016)
“The book provides a rich review of Bayesian nonparametric methods and models with a wealth of illustrations ranging from simple examples to more elaborated applications on case studies considered in recent literature. … the book succeeds in the difficult task of providing a rather complete, yet coincise, overview. Overall, the nature of the book makes it a suitable reference for both practitioners and theorists.” (Bernardo Nipoti, zbMATH 1333.62003, 2016)
“Methods are illustrated with a wealth of examples, ranging from stylised applications to case studies from recent literature. The bookis a good reference for statisticians interested in Bayesian non-parametric data analysis. It is well-written and structured. Readers can find the algorithms, examples and applications easy to follow and extremely useful. This book makes a good contribution to the literature in the area of Bayesian non-parametric statistics.” (Diego Andres Perez Ruiz, International Statistical Review, Vol. 84 (1), 2016)
“Book provides a brief overview and introduction of the subject, points to associated theoretical and applied literature, guides the interested reader to the most important and established methods in a wealth of methods where one can easily get lost, and encourages their application. At the same time, hints to the powerful and comprehensive R package DPpackage, which comprises most of the discussed methods in a unifying, easily accessible interface, greatly reduces the barriers to the use of nonparametric Bayesian methods.” (Manuel Wiesenfarth, Biometrical Journal, Vol. 58 (4), 2016)
Authors and Affiliations
About the authors
Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics.
Fernando Andrés Quintana is Professor in the Department of Statistics at Pontificia Universidad Catolica de Chile with interests in nonparametric Bayesian analysis and statistical computing. His publications include extensive work on clustering methods and applications in biostatistics.
Alejandro Jara is Associate Professor in the Department of Statistics at Pontificia Universidad Catolica de Chile, with research interests in nonparametric Bayesian statistics, Markov chain Monte Carlo methods and statistical computing. He developed the R package "DPpackage," a widely used public domain set of programs for inference under nonparametric Bayesian models.
Timothy Hanson is Professor of Statistics in the Department of Statistics at the University of South Carolina. His research interests include survival analysis, nonparametric regression
Bibliographic Information
Book Title: Bayesian Nonparametric Data Analysis
Authors: Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-3-319-18968-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-18967-3Published: 26 June 2015
Softcover ISBN: 978-3-319-36842-9Published: 15 October 2016
eBook ISBN: 978-3-319-18968-0Published: 17 June 2015
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XIV, 193
Number of Illustrations: 49 b/w illustrations, 10 illustrations in colour
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Statistics for Life Sciences, Medicine, Health Sciences