PubH 7440 home page

PubH 7440: Introduction to Bayesian Analysis (Spring 2012)

Instructor: Brad Carlin (e-mail: brad[*at*]biostat[*dot*]umn[*dot*]edu)

Course meetings: We will meet twice a week: Tu-Th 2:30pm - 3:45pm in Mayo C381 (SPH Computer Lab).

This is a 3 credit course. Prequisites are Stat 5101-02 or PubH 7405-7406 or instructor's consent. If you are unsure about your qualifications for the course, please contact the instructor.

Bayesian methods enable the combining of information from similar and independent experiments and also allow the incorporation of prior information in statistical analysis. This course introduces these methods, explains their practical implementation and compares them with classical (frequentist) methods. We emphasize data analysis via modern computer simulation methods and introduce the R and BUGS software packages, both of which are freely available and relatively easy to use.

The required text for the course is Bayesian Methods for Data Analysis, 3rd edition by Bradley P. Carlin and Thomas A. Louis. This book is availble from the University Bookstore, or over the web (either directly from the publisher, where you can use the ASA member 15% discount code, 634LH, or via amazon.com).

Here are some useful links:

Course information and syllabus
Homework assignments made to date, with due dates
Class blog maintained by the TA and featuring homework bug fixes, hints, solutions, and other helpful discussions
Many of the data sets and computer programs used in the book
The R software package for general statistical and graphical computing
WinBUGS, the leading software for Bayesian inference, the patch to upgrade to version 1.4.3, and the "immortality key" for unrestricted use,
BRugs, the R package for calling BUGS from R,

The following are optional text books for the course:

Lee, P.M. (2004). Bayesian Statistics: An Introduction, 3rd ed. London: Hodder-Arnold.

Spiegelhalter, D.J., Abrams, K.R. and Myles, J.P. (2004). Bayesian Approaches to Clinical Trials and Health Care Evaluation, Chichester: Wiley.

Gelman, A., Carlin, J., Stern, H. and Rubin, D.B. (2004). Bayesian Data Analysis, 2nd ed., Boca Raton, FL: Chapman and Hall/CRC Press.

Venables, W.N., Smith, D.M. and the R Development Core Team (2002). An Introduction to R: Revised and Updated.

Dalgaard, P. (2002). Introductory Statistics with R.

Faraway, J.J. (2005). Linear Models with R. Boca Raton, FL: Chapman and Hall/CRC Press.

Kruschke, J.K. (2011). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. New York: Academic Press.

The TA is Dr. Carlin's PhD student, Mr. Harrison Quick (e-mail: quic0038[*at*]umn[*dot*]edu).
His office hours are: Tues 11:00 am - 1:00 pm, and Thurs 12:00 - 2:00 pm, to be held in Mayo A446.
As mentioned above, Harrison maintains a class blog that contains helpful stuff for the class, including homework bug fixes, hints, and postings from the instructor and TA. Students can also participate in discussions pertaining to the class here.

The final grade will be based upon Homeworks (35%), two mid-terms (15% and 20% respectively) and a group final project presented orally at the end of the course (30%). Lecture notes for the course will be updated as the course proceeds and made available through the links below.