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 chapters are organized by traditional d...

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Bibliographic Details
Published in:Springer eBooks
Main Authors: Müller, Peter (Author), Quintana, Fernando Andres (Author), Jara, Alejandro (Author), Hanson, Tim (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2015.
Series:Springer Series in Statistics,
Subjects:
Online Access:http://dx.doi.org/10.1007/978-3-319-18968-0
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Table of Contents:
  • Preface
  • Acronyms
  • 1.Introduction
  • 2.Density Estimation - DP Models
  • 3.Density Estimation - Models Beyond the DP
  • 4.Regression
  • 5.Categorical Data
  • 6.Survival Analysis
  • 7.Hierarchical Models
  • 8.Clustering and Feature Allocation
  • 9.Other Inference Problems and Conclusions
  • Appendix: DP package.