文件名称:
Doing Bayesian Data Analysis: A Tutorial with R and BUGS
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文件大小: 9mb
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上传时间: 2017-10-11
详细说明： There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and B UGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and BUGS software Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). Coverage of experiment planning R and BUGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment 作者从概率统计和编程两方面入手，由浅入深地指导读者如何对实际数据进行贝叶斯分析。全书分成三部分，第一部分为基础篇：关于参数、概率、贝叶斯法则及R软件，第二部分为二元比例推断的基本理论，第三部分为广义线性模型。内容包括贝叶斯统计的基本理论、实验设计的有关知识、以层次模型和MCMC为代表的复杂方法等。同时覆盖所有需要用到非贝叶斯方法的情况，其中包括：t检验，方差分析（ANOVA）和ANOVA中的多重比较法，多元线性回归，Logistic回归，序列回归和卡方（列联表）分析。针对不同的学习目标（如R、BUGS等）列出了相应的重点章节；整理出贝叶斯统计中某些与传统统计学可作类比的内容，方便读者快速学习。本中提出的方法都是可操作的，并且所有涉及数学理论的地方都已经用实际例子非常直观地进行了解释。由于并不对读者的统计或编程基础有较高的要求，因此本书非常适合社会学或生物学研究者入门参考，同时也可作为相关科研人员的参考书。 ...展开收缩
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