ENGLISH

Bayesian Nonparametric Data Analysis

By Peter Müller , Fernando Andres Quintana , Alejandro Jara , Tim Hanson (auth.)

Book information

Publisher
Springer International Publishing
Year
2015
ISBN
978-3-319-18967-3, 978-3-319-18968-0
DOI
10.1007/978-3-319-18968-0
Language
english
Format
PDF
Filesize
5 MB (5187987 bytes)
Series
Springer Series in Statistics
Edition
1
Pages
193\203
Time added
2015-07-18 18:00:00
Health SciencesMedicineStatistical Theory and Methods; Statistics and Com

Description

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 chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. 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.

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