I teach from my notes,
not from a textbook. The material I cover follows the Hoff text
fairly closely, but the Hoff text is not as readable as I would
like. Most students like to have a text as a supplement to my
notes. The first two listed texts have electronic versions
available from the library.
(recommended): This book, published in 2009, provides about the
right level of coverage and is a reasonably up-to-date
treatment. An electronic
edition of this book is available to George Mason University
students and faculty from the university library.
Computer code is available at the link below for most of the
examples in the book.
Hoff, Peter D., A First
Course in Bayesian Statistical Methods. Springer,
Secondary text (recommended): This recently published book was written primarily for social scientists. It is accessible, well-written, and gives a comprehensive treatment beginning from the very basics through sophisticated hierarchical Bayesian models. An electronic edition of this book is available to George Mason University students and faculty from the university library. Computer code is available at the github site for most of the examples in the book.
(recommended): This comprehensive text has become the standard
reference in Bayesian statistical methods. The hyperlink below
contains reviews, exercises, data sets and software.
Gelman, A., Carlin, J., Stern, H., Dunson, D. B., Vehtari, A. and Rubin, D., Bayesian Data Analysis (3rd edition). CRC Press, 2013.
Supplemental text (recommended): This recently published book provides comprehensive coverage of computational Bayesian statistics with a focus on conducting Bayesian analyses of real data sets. The range of topics covered is much more extensive than the Hoff text, and will serve as a useful supplement for readers interested in Bayesian treatment of topics not covered in this course, such as generalized linear models, capture-recapture experiments, time series and image analysis. R code and a solution manual are available.
Grades will be based on the following:
Midterm exam (take-home) 35%
Final exam (take-home) 35%
The topics are listed below, along with
readings from the Hoff text. The
take-home midterm exam will be posted by March 2 and will be due
on Monday, March 21. The exam will include all material covered
prior to March 14. The final exam will be due on Monday,
May 16 at 11:59 PM.
The final exam will be cumulative.
|Unit 1||A Brief Tour of Bayesian
Inference and Decision Theory
||Week 1||Hoff, Chapter 1
|Unit 2||Random variables, Parametric
Models and Inference from Observation
||Weeks 2-3||Hoff, Chapter 2|
|Unit 3||Bayesian Inference with Conjugate Pairs: Single Parameter Models||Weeks 3-5||Hoff, Chapter 3
|Unit 4||Introduction to Monte Carlo
||Weeks 5-6||Hoff, Chapter 4
|Unit 5||The Normal Model
||Week 6-7||Hoff, Chapter 5
Chain Monte Carlo
||Hoff, Chapters 6 and 9
|Unit 7||Hierarchical Bayesian Models
||Week 10-11||Hoff, Chapter 8
|Unit 8||Bayesian Regression||Week 12||Hoff, Chapter 9
||Multinomial Distribution and
||Hypothesis Tests, Bayes Factors, and
Bayesian Model Averaging
||Hoff, Chapter 10