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Computational Bayesian Statistics : An Introduction.
・ISBN 978-1-108-48103-8 hard GB£ 120.00
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お気に入り
★★★
電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-1-108-64618-5
| 著者・編者 | Amaral Turkman, M. Antonia / Paulino, Carlos Daniel / Mueller, Peter, |
|---|---|
| シリーズ | Institute of Mathematical Statistics Textbooks |
| 出版社 | (Cambridge University Press, UK) |
| 出版年月 | 2019 |
| ページ数 | 254 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-9602＞ |
解説
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.