Bayesian Nonparametric Data Analysis [electronic resource] / by Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson.

By: Müller, Peter [author.]
Contributor(s): Quintana, Fernando Andres [author.] | Jara, Alejandro [author.] | Hanson, Tim [author.] | SpringerLink (Online service)
Material type: TextTextSeries: Springer Series in Statistics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XIV, 193 p. 59 illus., 10 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319189680Subject(s): Statistics | Statistics | Statistical Theory and Methods | Statistics and Computing/Statistics Programs | Statistics for Life Sciences, Medicine, Health SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 519.5 LOC classification: QA276-280Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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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