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 BUGS, 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 作者從概率統計和編程兩方面入手，由淺入深地指導讀者如何對實際數據進行貝葉斯分析。全書分成三部分，第一部分為基礎篇：關于參數、概率、貝葉斯法則及R軟件，第二部分為二元比例推斷的基本理論，第三部分為廣義線性模型。內容包括貝葉斯統計的基本理論、實驗設計的有關知識、以層次模型和MCMC為代表的復雜方法等。同時覆蓋所有需要用到非貝葉斯方法的情況，其中包括：t檢驗，方差分析（ANOVA）和ANOVA中的多重比較法，多元線性回歸，Logistic回歸，序列回歸和卡方（列聯表）分析。針對不同的學習目標（如R、BUGS等）列出了相應的重點章節；整理出貝葉斯統計中某些與傳統統計學可作類比的內容，方便讀者快速學習。本中提出的方法都是可操作的，并且所有涉及數學理論的地方都已經用實際例子非常直觀地進行了解釋。由于并不對讀者的統計或