Doing Bayesian Data Analysis

There is an explosion of Bayesian statistics. Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, provides an accessible approach to Bayesian Data Analysis, as it is explained with concrete examples. The book begins with the basics, and the successive concepts of probability and random sampling, and progressive to advanced hierarchical modeling methods for realistic data. The text provides comprehensive coverage of all scenarios by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis).

This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.

Accessible, including the basics of probability and random sampling

(UNIQUE) Examples with Rong language and BUGS software

Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis).

Coverage of experiment planning

R and BUGS computer programming code on website

video's that correspond to various parts of the book

graded difficulty level of exercises - easier to hardest

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, aka Change of Variables; references; index