---

＜電子ブック＞
Bayesian Nonparametric Data Analysis / by Peter Müller, Fernando Andres Quintana, Alejandro Jara, Tim Hanson
(Springer Series in Statistics. ISSN:2197568X)

版	1st ed. 2015.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2015
本文言語	英語
大きさ	XIV, 193 p. 59 illus., 10 illus. in color : online resource
著者標目	*Müller, Peter author Quintana, Fernando Andres author Jara, Alejandro author Hanson, Tim author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Mathematical statistics -- Data processing  全ての件名で検索 LCSH:Biometry FREE:Statistical Theory and Methods FREE:Statistics and Computing FREE:Biostatistics
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-319-18968-0

 類似資料