A First Course in Bayesian Statistical Methods - Springer...

Springer Texts in Statistics
Series Editors:
G. Casella
S. Fienberg
I. Olkin
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Peter D. Hoff
A First Course in Bayesian
Statistical Methods
123

Peter D. Hoff
Department of Statistics
University of Washington
Seattle WA 98195-4322
USA
[email protected]
ISSN 1431-875X
ISBN 978-0-387-92299-7 e-ISBN 978-0-387-92407-6
DOI 10.1007/978-0-387-92407-6
Springer Dordrecht Heidelberg London New York
Library of Congress Control Number: 2009929120
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Preface
This 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 course
is to familiarize the students with the basic concepts of Bayesian theory and
to quickly get them performing their own data analyses using Bayesian com-
putational tools. The audience for this course includes non-statistics graduate
students who did well in their department’s graduate-level introductory statis-
tics courses and who also have an interest in statistics. Additionally, first- and
second-year statistics graduate students have found this course to be a useful
introduction to statistical modeling. Like the course, this book is intended to
be a self-contained and compact introduction to the main concepts of Bayesian
theory and practice. By the end of the text, readers should have the ability to
understand and implement the basic tools of Bayesian statistical methods for
their own data analysis purposes. The text is not intended as a comprehen-
sive handbook for advanced statistical researchers, although it is hoped that
this latter category of readers could use this book as a quick introduction to
Bayesian methods and as a preparation for more comprehensive and detailed
studies.
Computing
Monte Carlo summaries of posterior distributions play an important role in
the way data analyses are presented in this text. My experience has been
that once a student understands the basic idea of posterior sampling, their
data analyses quickly become more creative and meaningful, using relevant
posterior predictive distributions and interesting functions of parameters. The
open-source R