Peter Müller & Fernando Andres Quintana 
Bayesian Nonparametric Data Analysis [PDF ebook] 

Ủng hộ

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.

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Mục lục

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.

Giới thiệu về tác giả

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

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Ngôn ngữ Anh ● định dạng PDF ● Trang 193 ● ISBN 9783319189680 ● Kích thước tập tin 5.3 MB ● Nhà xuất bản Springer International Publishing ● Thành phố Cham ● Quốc gia CH ● Được phát hành 2015 ● Có thể tải xuống 24 tháng ● Tiền tệ EUR ● TÔI 4410132 ● Sao chép bảo vệ DRM xã hội

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