Bayesian Data Analysis, 3/e (Hardcover)

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin

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商品描述

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

 

 

  • Four new chapters on nonparametric modeling
  • Coverage of weakly informative priors and boundary-avoiding priors
  • Updated discussion of cross-validation and predictive information criteria
  • Improved convergence monitoring and effective sample size calculations for iterative simulation
  • Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
  • New and revised software code

 

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

商品描述(中文翻譯)

《贏得2016年國際貝葉斯分析學會德格魯特獎的得主》

這本經典書籍的第三版被廣泛認為是貝葉斯方法的領先教材,因其易於理解、實用的方法來分析數據和解決研究問題而受到讚譽。《貝葉斯數據分析,第三版》繼續以應用的方式使用最新的貝葉斯方法進行分析。作者們都是統計學界的領袖,他們從數據分析的角度介紹基本概念,然後介紹高級方法。在整本書中,大量來自實際應用和研究的實例強調了貝葉斯推斷在實踐中的應用。

《第三版的新內容》

- 四個關於非參數建模的新章節
- 弱信息先驗和避免邊界先驗的涵蓋
- 交叉驗證和預測信息準則的更新討論
- 改進的收斂監控和迭代模擬的有效樣本大小計算
- 演示哈密爾頓蒙特卡羅、變分貝葉斯和期望傳播
- 新的和修訂的軟件代碼

這本書可以以三種不同的方式使用。對於本科生,它從基本原理介紹貝葉斯推斷。對於研究生,本書介紹了統計學和相關領域中有效的當前貝葉斯建模和計算方法。對於研究人員,它提供了應用統計學中各種貝葉斯方法。書中還提供了額外的資料,包括示例中使用的數據集、選定練習的解答和軟件使用說明,這些資料可以在書的網頁上獲得。