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Bayesian Nonparametric Data Analysis
ISBN-13:
9783319189680
Veröffentl:
2015
Seiten:
193
Autor:
Peter Müller
Serie:
Springer Series in Statistics
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
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.
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.
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