Book description
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are stepbystep instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact highlevel scripts that make it easy to run the programs on your own data sets.
The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metricpredicted variable on one or two groups; metricpredicted variable with one metric predictor; metricpredicted variable with multiple metric predictors; metricpredicted variable with one nominal predictor; and metricpredicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment.
This book is intended for firstyear graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.
 Accessible, including the basics of essential concepts of probability and random sampling
 Examples with R programming language and JAGS 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 JAGS computer programming code on website
 Exercises have explicit purposes and guidelines for accomplishment
 Provides stepbystep instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Table of contents
 Cover image
 Title page
 Table of Contents
 Copyright
 Dedication
 Chapter 1: What's in This Book (Read This First!)
 Part I: The Basics: Models, Probability, Bayes’ Rule, and R

Part II: All the Fundamentals Applied to Inferring a Binomial Probability
 Introduction
 Chapter 6: Inferring a Binomial Probability via Exact Mathematical Analysis
 Chapter 7: Markov Chain Monte Carlo

Chapter 8: JAGS
 8.1 Jags and its relation to R
 8.2 A complete example
 8.3 Simplified scripts for frequently used analyses
 8.4 Example: difference of biases
 8.5 Sampling from the prior distribution in jags
 8.6 Probability distributions available in jags
 8.7 Faster sampling with parallel processing in runjags
 8.8 Tips for expanding jags models
 8.9 Exercises
 Chapter 9: Hierarchical Models
 Chapter 10: Model Comparison and Hierarchical Modeling
 Chapter 11: Null Hypothesis Significance Testing
 Chapter 12: Bayesian Approaches to Testing a Point (“Null”) Hypothesis
 Chapter 13: Goals, Power, and Sample Size
 Chapter 14: Stan

Part III: The Generalized Linear Model
 Introduction
 Chapter 15: Overview of the Generalized Linear Model
 Chapter 16: MetricPredicted Variable on One or Two Groups
 Chapter 17: Metric Predicted Variable with One Metric Predictor
 Chapter 18: Metric Predicted Variable with Multiple Metric Predictors
 Chapter 19: Metric Predicted Variable with One Nominal Predictor

Chapter 20: Metric Predicted Variable with Multiple Nominal Predictors
 20.1 Describing groups of metric data with multiple nominal predictors
 20.2 Hierarchical bayesian approach
 20.3 Rescaling can change interactions, homogeneity, and normality
 20.4 Heterogeneous variances and robustness against outliers
 20.5 Withinsubject designs
 20.6 Model comparison approach
 20.7 Exercises
 Chapter 21: Dichotomous Predicted Variable
 Chapter 22: Nominal Predicted Variable
 Chapter 23: Ordinal Predicted Variable
 Chapter 24: Count Predicted Variable
 Chapter 25: Tools in the Trunk
 Bibliography
 Index
Product information
 Title: Doing Bayesian Data Analysis, 2nd Edition
 Author(s):
 Release date: November 2014
 Publisher(s): Academic Press
 ISBN: 9780124059160
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