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.

Reviews

“I think it fills a gaping hole in what is currently available, and will serve to create its own market as researchers and their students transition towards the routine application of Bayesian statistical methods.? -Prof. Michael lee, University of California, Irvine, and president of the Society for Mathematical Psychology

“Kruschke’s text covers a much broader range of traditional experimental designs…has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" -Prof. Geoffrey Iverson, University of California, Irvine, and past president of the Society for Mathematical Psychology

“John Kruschke has written a book on Statistics. It’s better than others for reasons stylistic. It also is better because itis Bayesian. To find out why, buy it -- it’s truly amazin’!?-James L. (Jay) McClelland, Lucie Stern Professor & Chair, Dept. Of Psychology, Standford University

Feature

• 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

This Book's Organization: Read me First!; The Basics: Parameters, Probability, Bayes' Rule and R; What is this stuff called probability?; Bayes' Rule; Part II All the Fundamental Concepts and Techniques in a Simple Scenario; Inferring a Binomial Proportion via Exact mathematical Analysis; Inferring a Binomial Proportion via Grid Approximation; Inferring a Binomial Proportion via Monte Carlo Methods; Inferences Regarding Two Binomial Proportions; Bernoulli Likelihood with Hierarchical Prior; Hierarchical modeling and model comparison; Null Hypothesis Significance Testing; Bayesian Approaches to Testing a Point ("Null") Hypothesis; Goals, Power, and Sample Size; Part III The Generalized Linear Model; Overview of the Generalized Linear Model; Metric Predicted Variable on a Single Group; Metric Predicted Variable with One Metric Predictor; Metric Predicted Variable with Multiple Metric Predictors; Metric Predicted Variable with One Nominal Predictor; Metric Predicted Variable with Multiple Nominal Predictors; Dichotomous Predicted Variable; Original Predicted Variable, Contingency Table Analysis; Part IV Tools in the Trunk; Reparameterization, a.k.a. Change of Variables; References; Index