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Bayesian nonparametric data analysis / Peter Muller...[et al.].

By: Contributor(s): Series: Springer series in statisticsPublication details: Switzerland : Springer, 2015.Description: xiv, 193 p. : illustrations (some color) ; 24 cmISBN:
  • 9783319189673
Subject(s): DDC classification:
  • 000SA.161 23 M958
Contents:
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 -- A. DP package-- Index.
Summary: 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 on-line software pages.
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Includes bibliographical references and index.

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 --
A. DP package--
Index.

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 on-line software pages.

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