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商品詳細
Bayesian Nonparametric Data Analysis. Softcover reprint of the original 1st ed. 2015.
・ISBN 978-3-319-36842-9 paper EUR 109.99
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お気に入り
★★★
| 著者・編者 | Mueller, Peter / Quintana, Fernando Andres / Jara, Alejandro / Hanson, Tim, |
|---|---|
| シリーズ | Springer Series in Statistics |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2016 |
| ページ数 | 193 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-21586＞ |
解説
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.