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Bayesian Hierarchical Models : With Applications Using R. 2nd ed. ベイズ階層モデル－Rを用いた応用 第2版
・ISBN 978-1-4987-8575-4 2019 hard GB£ 145.99
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・ISBN 978-1-032-17715-1 2021 paper GB£ 54.99
￥17,420.- (税込) ※（※）価格はご注文時の参考価格となります。
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-0-429-11335-2
| 著者・編者 | Congdon, Peter D., |
|---|---|
| 出版社 | (Chapman & Hall / CRC, US) |
| ページ数 | 580 pp. |
| 言語 | ENG |
| ニュース番号 | ＜652-446 652-P1036＞ |
解説
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods.
The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples.
The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities.
Features:
- Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling
- Includes many real data examples to illustrate different modelling topics
- R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation
- Software options and coding principles are introduced in new chapter on computing
- Programs and data sets available on the book's website