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Bayesian Nonparametric Data Analysis

Buch von Peter Mueller , Fernando Andrés Quintana , Alejandro Jara und Tim Hanson

76600838
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Details
Artikel-Nr.:
76600838
Im Sortiment seit:
14.07.2015
Erscheinungsdatum:
26.06.2015
Medium:
Buch
Einband:
Gebunden
Autor:
Mueller, Peter
Quintana, Fernando Andrés
Jara, Alejandro
Hanson, Tim
Verlag:
Springer-Verlag GmbH
Springer International Publishing
Sprache:
Englisch
Rubrik:
Mathematik
Wahrscheinlichkeitstheorie
Seiten:
193
Abbildungen:
49 schwarz-weiße und 10 farbige Abbildungen, Bibliographie
Herkunft:
NIEDERLANDE (NL)
Reihe:
Springer Texts in Statistics
Informationen:
Book
Gewicht:
458 gr
Beschreibung
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.
Information
This is the first text to introduce nonparametric Bayesian inference from a data analysis perspective Includes a large number of examples to illustrate the application of nonparametric Bayesian models for important statistical inference Problems Features an extensive discussion of computational details for a practical implementation, including R code for many of the examples
Information zum Autor
Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics. Fernando Andrés Quintana is Professor in the Department of Statistics at Pontificia Universidad Catolica de Chile with interests in nonparametric Bayesian analysis and statistical computing. His publications include extensive work on clustering methods and applications in biostatistics. Alejandro Jara is Associate Professor in the Department of Statistics at Pontificia Universidad Catolica de Chile, with research interests in nonparametric Bayesian statistics, Markov chain Monte Carlo methods and statistical computing. He developed the R package "DPpackage," a widely used public domain set of programs for inference under nonparametric Bayesian models. Timothy Hanson is Professor of Statistics in the Department of Statistics at the University of South Carolina. His research interests include survival analysis, nonparametric regression
Inhaltsverzeichnis
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.
Bilder