There is an explosion of interest in Bayesian statistics primarily because recently created
computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing
Bayesian Data Analysis: A Tutorial with R JAGS and Stan provides an accessible approach to
Bayesian data analysis as material is explained clearly with concrete examples. The book
begins with the basics including essential concepts of probability and random sampling and
gradually progresses to advanced hierarchical modeling methods for realistic data. Included are
step-by-step instructions on how to conduct Bayesian data analyses in the popular and free
software R and WinBugs. This book is intended for first-year graduate students or advanced
undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods
for data analysis which is becoming the accepted research standard. Knowledge of algebra and
basic calculus is a prerequisite. New to this Edition (partial list): * There are all 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 high-level scripts that make
it easy to run the programs on your own data sets. This new programming was a major undertaking
by itself.* The introductory Chapter 2 regarding the basic ideas of how Bayesian inference
re-allocates credibility across possibilities is completely rewritten and greatly expanded.*
There are completely new chapters on the programming languages R (Ch. 3) JAGS (Ch. 8) and
Stan (Ch. 14). The lengthy new chapter on R includes explanations of data files and structures
such as lists and data frames along with several utility functions. (It also has a new poem
that I am particularly pleased with.) The new chapter on JAGS includes explanation of the
RunJAGS package which executes JAGS on parallel computer cores. The new chapter on Stan
provides a novel explanation of the concepts of Hamiltonian Monte Carlo. The chapter on Stan
also explains conceptual differences in program flow between it and JAGS.* Chapter 5 on Bayes'
rule is greatly revised with a new emphasis on how Bayes' rule re-allocates credibility across
parameter values from prior to posterior. The material on model comparison has been removed
from all the early chapters and integrated into a compact presentation in Chapter 10.* What
were two separate chapters on the Metropolis algorithm and Gibbs sampling have been
consolidated into a single chapter on MCMC methods (as Chapter 7). There is extensive new
material on MCMC convergence diagnostics in Chapters 7 and 8. There are explanations of
autocorrelation and effective sample size. There is also exploration of the stability of the
estimates of the HDI limits. New computer programs display the diagnostics as well.* Chapter 9
on hierarchical models includes extensive new and unique material on the crucial concept of
shrinkage along with new examples.* All the material on model comparison which was spread
across various chapters in the first edition in now consolidated into a single focused chapter
(Ch. 10) that emphasizes its conceptualization as a case of hierarchical modeling.* Chapter 11
on null hypothesis significance testing is extensively revised. It has new material for
introducing the concept of sampling distribution. It has new illustrations of sampling
distributions for various stopping rules and for multiple tests.* Chapter 12 regarding
Bayesian approaches to null value assessment has new material about the region of practical
equivalence (ROPE) new examples of accepting the null value by Bayes factors and new
explanation of the Bayes factor in terms of the Savage-Dickey method.* Chapter 13 regarding
statistical power and sample size has an extensive new section on sequential testing and
making the research goal be precision of estimation instead of rejecting or accepting a
particular value.* Chapter 15 which introduces the generalized linear model is fully revised
with more complete tables showing combinations of predicted and predictor variable types.*
Chapter 16 regarding estimation of means now includes extensive discussion of comparing two
groups along with explicit estimates of effect size.* Chapter 17 regarding regression on a
single metric predictor now includes extensive examples of robust regression in JAGS and Stan.
New examples of hierarchical regression including quadratic trend graphically illustrate
shrinkage in estimates of individual slopes and curvatures. The use of weighted data is also
illustrated.* Chapter 18 on multiple linear regression includes a new section on Bayesian
variable selection in which various candidate predictors are probabilistically included in the
regression model.* Chapter 19 on one-factor ANOVA-like analysis has all new examples
including a completely worked out example analogous to analysis of covariance (ANCOVA) and a
new example invo
