Doing Bayesian Data Analysis (2/e)

John Kruschke

出版时间

2014-11-01

ISBN

9780124058880

评分

★★★★★
书籍介绍
在贝叶斯统计的入门书单里,它常被与 Gelman 的《Bayesian Data Analysis》、Gelman 的《Statistical Rethinking》并列称为
作者简介
John K. Kruschke is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics, at Indiana University in Bloomington, Indiana, USA. He is eight-time winner of Teaching Excellence Recognition Awards from Indiana University. He won the Troland Research Award from the National Academy of Sciences (USA), and the Remak Distinguished Scholar Award from Indiana University. He has been on the editorial boards of various scientific journals, including Psychological Review, the Journal of Experimental Psychology: General, and the Journal of Mathematical Psychology, among others. After attending the Summer Science Program as a high school student and considering a career in astronomy, Kruschke earned a bachelor's degree in mathematics (with high distinction in general scholarship) from the University of California at Berkeley. As an undergraduate, Kruschke taught self-designed tutoring sessions for many math courses at the Student Learning Center. During graduate school he attended the 1988 Connectionist Models Summer School, and earned a doctorate in psychology also from U.C. Berkeley. He joined the faculty of Indiana University in 1989. Professor Kruschke's publications can be found at his Google Scholar page. His current research interests focus on moral psychology. Professor Kruschke taught traditional statistical methods for many years until reaching a point, circa 2003, when he could no longer teach corrections for multiple comparisons with a clear conscience. The perils of p values provoked him to find a better way, and after only several thousand hours of relentless effort, the 1st and 2nd editions of Doing Bayesian Data Analysis emerged.
用户评论
作者还是我大IU的
有一点理论,大量的例子和R代码,但是读完也不好说很会实操,倾向于用sas的mcmc,比较简单一些。
偏基础,一些关于MCMC啥的intuition讲的不错,主要是用贝叶斯重构基础的frequency统计分析
之前一直都在啃大部头的PRML, 大部头总是太关注理论的完整性,对例子和怎么实现太过于吝啬。这本简单,十分强调代码与例子,对于搞应用的人来说是最适宜不过了。 另外,简单的书反而能把bayesian model里的一些核心哲学突出出来。看着posterier probability 改变和原始prior折衷的过程,仿佛看到了人的思维改变的过程。
感觉还蛮不错的。可惜有事不能读完了
读的第一本贝叶斯。封面还挺萌萌哒的,确实是给没有基础的人看的。有时候过度压缩数学也不好,mcmc这里讲的比较浅,看一半跳去看Gelman那本的mcmc部分,发现清楚很多,又找了Stanford计算机科学一门课的课件终于搞懂了。但是没什么用,因为教材总是过时的,NUTS比这些基础算法复杂很多,调整抽样有效性,解决不收敛的技术也不一样,比如用非中心化参数解决divergent transition这种问题教材根本不讲,当然这本书太老了主要用的不是Stan。各种抽样,编程还是直接看Stan官方文档比较好。接下来要跟rethinking2023课程和ubc研究生贝叶斯统计课程。读完这本我以为是基本不具备上手能力的,比如写个hierarchical高斯过程回归这种。而且这本书模型比较知识点太少。。
十分适合心理学子😂
还挺通俗易懂的~
适合非数统背景入门贝叶斯方法, 但是太罗嗦了
作者是贝叶斯原教旨主义者
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