PrefaceThis book originated from a set of lecture notes for a one-quarter graduate-level course taught at the University of Washington. The purpose of the courseis to familiarize the students with the basic concepts of Bayesian theory andto quickly get them performing their own data analyses using Bayesian com-putational tools. The audience for this course includes non-statistics graduatestudents who did well in their department’s graduate-level introductory statis-tics courses and who also have an interest in statistics. Additionally, first- andsecond-year statistics graduate students have found this course to be a usefulintroduction to statistical modeling. Like the course, this book is intended tobe a self-contained and compact introduction to the main concepts of Bayesiantheory and practice. By the end of the text, readers should have the ability tounderstand and implement the basic tools of Bayesian statistical methods fortheir own data analysis purposes. The text is not intended as a comprehen-sive handbook for advanced statistical researchers, although it is hoped thatthis latter category of readers could use this book as a quick introduction toBayesian methods and as a preparation for more comprehensive and detailedstudies.ComputingMonte Carlo summaries of posterior distributions play an important role inthe way data analyses are presented in this text. My experience has beenthat once a student understands the basic idea of posterior sampling, theirdata analyses quickly become more creative and meaningful, using relevantposterior predictive distributions and interesting functions of parameters. Theopen-sourceR