Book description
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 nonbayesian textbooks ttests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chisquare (contingency table analysis).
Coverage of experiment planning
R and BUGS computer programming code on website
Exercises have explicit purposes and guidelines for accomplishment
Table of contents
 Cover image
 Title page
 Table of Contents
 Copyright
 Dedication
 Chapter 1. This Book’s Organization
 Part 1: The Basics: Parameters, Probability, Bayes’ Rule, and R

Part 2: All the Fundamentals Applied to Inferring a Binomial Proportion
 Chapter 5. Inferring a Binomial Proportion via Exact Mathematical Analysis
 Chapter 6. Inferring a Binomial Proportion via Grid Approximation
 Chapter 7. Inferring a Binomial Proportion via the Metropolis Algorithm
 Chapter 8. Inferring Two Binomial Proportions via Gibbs Sampling
 Chapter 9. Bernoulli Likelihood with Hierarchical Prior
 Chapter 10. Hierarchical Modeling and Model Comparison
 Chapter 11. Null Hypothesis Significance Testing
 Chapter 12. Bayesian Approaches to Testing a Point (“Null”)Hypothesis
 Chapter 13. Goals, Power, and Sample Size

Part 3: Applied to the Generalized Linear Model
 Chapter 14. Overview of the Generalized Linear Model
 Chapter 15. Metric Predicted Variable on a Single Group
 Chapter 16. Metric Predicted Variable with One Metric Predictor
 Chapter 17. Metric Predicted Variable with Multiple Metric Predictors
 Chapter 18. Metric Predicted Variable with One Nominal Predictor
 Chapter 19. Metric Predicted Variable with Multiple Nominal Predictors
 Chapter 20. Dichotomous Predicted Variable
 Chapter 21. Ordinal Predicted Variable
 Chapter 22. Contingency Table Analysis
 Chapter 23. Tools in the Trunk
 Index
Product information
 Title: Doing Bayesian Data Analysis
 Author(s):
 Release date: November 2010
 Publisher(s): Academic Press
 ISBN: 9780123814869
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