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Bayesian Computation with R (Use R) ペーパーバック – 2007/7/1
購入オプションとあわせ買い
There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.
Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.
This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book.
The second edition contains several new topics such as the use of mixtures of conjugate priors and the use of Zellner’s g priors to choose between models in linear regression. There are more illustrations of the construction of informative prior distributions, such as the use of conditional means priors and multivariate normal priors in binary regressions. The new edition contains changes in the R code illustrations according to the latest edition of the LearnBayes package.
- 本の長さ267ページ
- 言語英語
- 出版社Springer Verlag
- 発売日2007/7/1
- 寸法15.24 x 1.27 x 22.86 cm
- ISBN-100387713840
- ISBN-13978-0387713847
商品の説明
レビュー
登録情報
- 出版社 : Springer Verlag (2007/7/1)
- 発売日 : 2007/7/1
- 言語 : 英語
- ペーパーバック : 267ページ
- ISBN-10 : 0387713840
- ISBN-13 : 978-0387713847
- 寸法 : 15.24 x 1.27 x 22.86 cm
- Amazon 売れ筋ランキング: - 758,109位洋書 (洋書の売れ筋ランキングを見る)
- - 279位Number Systems (洋書)
- - 1,347位Computer Modeling & Simulation
- - 1,349位Information Theory
- カスタマーレビュー:
-
トップレビュー
上位レビュー、対象国: 日本
レビューのフィルタリング中に問題が発生しました。後でもう一度試してください。
特にRを使うことはプログラミングを学ぶという副次効果も期待できるので、その意味でも残念です。
とはいえ、これを読めばベイズが多少分かることは事実です。学部生向けの入門書としては良いと思います。とかく理論だけに傾きがちなベイズ本をとは違って、実践を考えた点はかなり評価できるのではないでしょうか。
他の国からのトップレビュー
It is a product for advanced statistician.
It takes a step-by-step approach, using straightforward practical examples immediately.
It introduces built-in R functions appropriate to each specific example and reviews them briefly at the end of each one.
It starts with some simple standard descriptive statistical examples and includes useful graphical R plotting functions to display helpful diagrams. It assumes you have intermediate statistical knowledge and can move beyond the simple descriptive stats work with relative ease. It then uses these concepts to introduce Bayesian statistics after a very short introduction. In effect, you should already be aware of the principles of the Bayesian approach (initial belief modelled via prior distributions, these modified through measurements expressed in posterior distributions which are then used to draw inferences). I had some of this knowledge, but swiftly realised I would need to devote serious study to grasp it thoroughly. This is an excellent book, with an extensive bibliography, to help you along the way.