Bayesian Computation with R 2024 pdf epub mobi 電子書下載

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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. Also the book 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.

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發表於2024-12-31

Bayesian Computation with R 2024 pdf epub mobi 電子書下載

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Bayesian Computation with R pdf epub mobi 讀後感

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感覺超級好的textbook，雖然一直不習慣R，當時還是把書上的code跑瞭過半，感覺對理解bayesian超級有幫助。不像其他學科，初學bayesian應該一開始就和computer結閤，不然真的很沒趣。這本書沒太多理論，提供大量操作，循序漸進，由簡單到復雜，初學bayesian如果能結閤這本書一起...

評分☆☆☆☆☆

作者有點強推自己寫的R包瞭，對bayesian的理論思想講的不夠清楚，適閤有一定理論基礎的同學看，學習如何實現MCMC，推薦先看Bayesian data analysis。其實bayesian相比frequentist理論上要簡單的多，無論是估計，檢驗，還是迴歸，無非就是先驗，likelihood，後驗的套路。

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出版者:Springer

作者:Jim Albert

出品人:

頁數:270

譯者:

出版時間:2008-7-9

價格:GBP 34.99

裝幀:Paperback

isbn號碼:9780387713847

叢書系列:Use R