This is a 3 credit course. Prequisites are Stat 5101-02 or PubH 8465-66 or instructor's consent. You also must be a graduate student either in Public Health or in Statistics. If you do not meet any of the above qualifications, please come and see the instructor in person.
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).
There will be two recommended text books for the course (not required). They are:
The TA is Xiuan Liu (e-mail:xiaunliu@biostat.umn.edu). His office hours are: Monday, 2:25pm--5:00pm, starting immediately after our class.
The final grade will be based upon Homeworks (50%), a mid-term (20%) and a case-study project presentation at the end of the course (30%). Other details about the course (including a detailed syllabus) may be found through the following link (a pdf file).