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商品詳細
Bayesian Regression and Causal Inference : With Examples in R.
・ISBN 978-3-032-19222-6 hard EUR 109.99
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| 著者・編者 | Ponnoprat, Donlapark, |
|---|---|
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2026 |
| ページ数 | 241 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-21714＞ |
解説
This textbook provides a practical guide to the Bayesian framework for data modeling and causal inference, focusing on model interpretation, diagnostics, and uncertainty quantification. Central to the book is a "learning-by-doing" approach, using concrete examples in R with real-world datasets spanning diverse fields, including education, psychology, medicine, behavioral science, and environmental science.
The book is structured into three parts:
? Part I: Linear Regression - Learn the basics of Bayesian linear regression, model diagnostics, and uncertainty quantification through a probabilistic lens.
? Part II: Generalized Linear Models - Extend your modeling toolkit to handle binary and count data, zero-inflated models, and clustered data structures common in longitudinal studies.
? Part III: Causal Inference - Learn to identify treatment effects from non-experimental data. This section explores classical techniques-including inverse probability weighting, doubly robust estimation, instrumental variables, and difference-in-differences-alongside advanced techniques like synthetic control, doubly robust DiD, and synthetic DiD.