PubH 7440 Introduction to Bayesian Analysis - Spring 2008

Instructors: Brad Carlin (e-mail: brad@biostat.umn.edu) and Sudipto Banerjee (e-mail: sudiptob@biostat.umn.edu)

We will meet twice a week. Tue-Thu 2:30pm - 3:45pm in Mayo C381 (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 one of the instructors.

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 WinBUGS software (free) and the R software (almost like a free S-plus).

The required text for the course will be the beta-test version of Bayesian Methods for Data Analysis, 3rd edition by Bradley P. Carlin and Thomas A. Louis. This will be passed out along with the syllabus on the first day of class.

The syllabus can also be downloaded HERE.

The following are optional text books for the course:

  • Lee, P. M. (2004). Bayesian Statistics Publisher: Hodder Arnold
  • Spiegelhalter, D.J., Abrams, K.R. and Myles, J.P. Bayesian Approaches to Clinical Trials and Health Care Evaluation Chichester: John Wiley and Sons Ltd.
  • Ghosh, J.K., Delampady, M. and Samanta, T. (2006). An Introduction to Bayesian Analysis. Theory and Methods. New York: Springer.
  • Carlin, B.P. and Louis, T.A. (2000).Bayes and Empirical Bayes Methods for Data Analysis.Second Edition. Publisher: CRC/Chapman and Hall.
  • 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. Publisher: CRC/Chapman and Hall.

    Notes will be provided regularly to supplement the texts.

    Some useful datasets to be used in the course can be found HERE.

    The web sites for the two softwares needed for the course are:

  • WinBUGS or OpenBUGS
  • You can download the new registration key for WinBUGS from HERE. NOTE THAT YOU DO NOT REQUIRE ANY REGISTRATION KEY FOR OPENBUGS.
  • R

    The TA is Laura Hatfield (e-mail: hatfield@umn.edu). Her office hours are: Tuesdays and Thursdays, 9:30am--11:00am at Mayo A452.

    There is a CLASS BLOG HERE that contains helpful stuff for the class including homework postings from the TA and students can participate in discussions pertaining to the class.

    The final grade will be based upon Homeworks (30%), two mid-terms (15% and 25% respectively) and a written project 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.