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.
Author(s): Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson (auth.)
Series: Springer Series in Statistics
Edition: 1
Publisher: Springer International Publishing
Year: 2015
Language: English
Pages: 193
Tags: Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Statistics for Life Sciences, Medicine, Health Sciences
Front Matter....Pages i-xiv
Introduction....Pages 1-5
Density Estimation: DP Models....Pages 7-31
Density Estimation: Models Beyond the DP....Pages 33-50
Regression....Pages 51-75
Categorical Data....Pages 77-100
Survival Analysis....Pages 101-123
Hierarchical Models....Pages 125-143
Clustering and Feature Allocation....Pages 145-174
Other Inference Problems and Conclusion....Pages 175-178
Back Matter....Pages 179-193
