大葉大學圖書館 |

Bayesian nonparametric data analysis[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.542
書名/作者:	Bayesian nonparametric data analysis/ by Peter Muller ... [et al.].
其他作者:	Muller, Peter.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xiv, 193 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Bayesian statistical decision theory.
標題:	Statistics.
標題:	Statistical Theory and Methods.
標題:	Statistics and Computing/Statistics Programs.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
ISBN:	9783319189680 (electronic bk.)
ISBN:	9783319189673 (paper)
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-18968-0

Bayesian nonparametric data analysis[electronic resource] /
Bayesian nonparametric data analysis[electronic resource] /by Peter Muller ... [et al.]. - Cham :Springer International Publishing :2015. - xiv, 193 p. :ill., digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

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.

ISBN: 9783319189680 (electronic bk.)

Standard No.: 10.1007/978-3-319-18968-0doiSubjects--Topical Terms:

367145
Bayesian statistical decision theory.

LC Class. No.: QA279.5

Dewey Class. No.: 519.542

Bayesian nonparametric data analysis[electronic resource] /
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520 $a 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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