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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

1. Cover image
2. Title page
5. Dedication
6. Chapter 1. This Book’s Organization
7. Part 1: The Basics: Parameters, Probability, Bayes’ Rule, and R
1. Chapter 2. Introduction
2. Chapter 3. What Is This Stuff Called Probability?
3. Chapter 4. Bayes’ Rule
8. Part 2: All the Fundamentals Applied to Inferring a Binomial Proportion
1. Chapter 5. Inferring a Binomial Proportion via Exact Mathematical Analysis
2. Chapter 6. Inferring a Binomial Proportion via Grid Approximation
3. Chapter 7. Inferring a Binomial Proportion via the Metropolis Algorithm
4. Chapter 8. Inferring Two Binomial Proportions via Gibbs Sampling
5. Chapter 9. Bernoulli Likelihood with Hierarchical Prior
6. Chapter 10. Hierarchical Modeling and Model Comparison
7. Chapter 11. Null Hypothesis Significance Testing
8. Chapter 12. Bayesian Approaches to Testing a Point (“Null”)Hypothesis
9. Chapter 13. Goals, Power, and Sample Size
9. Part 3: Applied to the Generalized Linear Model
1. Chapter 14. Overview of the Generalized Linear Model
2. Chapter 15. Metric Predicted Variable on a Single Group
3. Chapter 16. Metric Predicted Variable with One Metric Predictor
4. Chapter 17. Metric Predicted Variable with Multiple Metric Predictors
5. Chapter 18. Metric Predicted Variable with One Nominal Predictor
6. Chapter 19. Metric Predicted Variable with Multiple Nominal Predictors
7. Chapter 20. Dichotomous Predicted Variable
8. Chapter 21. Ordinal Predicted Variable
9. Chapter 22. Contingency Table Analysis
10. Chapter 23. Tools in the Trunk
10. Index