The page below describes the 2005 version of the seminar. The 2006 version will use different materials. Much of the 2006 materials will be created by Prof. Kruschke. Here are some textbooks that you might find useful as alternative sources:
Albert and Rossman. Workshop Statistics: Discovery with Data, a Bayesian Approach.
The last few chapters of this book give a wonderfully clear introduction to the basics of Bayesian statistics --- but only the basics.
Bolstad. Introduction to Bayesian Statistics.
Terrific tutorial that uses only basic calculus; highly recommended. Only down side is that it does not cover numerical approximation methods.
Gill. Bayesian Methods: A Social and Behavioral Sciences Approach.
Gelman, Carlin, Stern and Rubin: Bayesian Data Analysis.
These two books offer more advanced examples of Bayesian methods, including Monte Carlo techniques.
This web page is at URL = http://www.indiana.edu/~jkkteach/P747_2005/
Brief Description: This course is an introduction
to Bayesian data analysis. We'll try to cover the first 11 chapters of
the textbook (see below). We will do a lot of computer simulation, not
just mathematical theory.
Format: This is a one-time seminar course, which means that class periods will be devoted to impromptu lectures, discussions of assignments, and computer examples. There will be regular homeworks. Peer grading of homework is an integral part of the course, because studying the key (generated by the instructor) and grading a peer's paper will enhance learning.
Pre-requisites. Students should have already taken a thorough course in "hypothesis testing" statistics (such as Psych P553, and preferably also P554, or equivalent). Familiarity with basic integral calculus and linear algebra will help. We will also be programming statistical analyses, so previous experience with a language such as Matlab will help.
Time and Place: Fall semester, 2005. Wednesdays 9:00-11:00am. Psychology Building, room 111.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). Bayesian Data Analysis, 2nd Edition. Boca Raton, FL: CRC Press.
We will not be using the books listed below, but if you would like supplementary material, you might consider this less mathematical and lower level introduction:
Berry, D. A. (1996). Statistics: A Bayesian Perspective. Belmont, CA: Duxbury Press.
Or, this excellent but very elementary introduction to statistics with a few final chapters on Bayesian methods:
Albert, J. H. and Rossman, A. J. (2001). Workshop Statistics: Discovery with Data, a Bayesian Approach. Emeryville, CA: Key College Publishing.
Homework: Regular homework will be assigned. Some
exercises will be from the textbook. The authors have selected answers
available at their website.
Software: We will do some introductory analyses in
Excel or Matlab, but most analyses will be done in the language "R".
Oncourse: Announcements and homework keys will be posted on Oncourse (new window). Please check often.
Schedule. As this is a one-time (and first-time) course, the schedule will emerge as we go. The following table is continually under construction.
|Week #, Date||Topic, Assignment, Etc.|
|1: Aug. 31||
|2: Sep. 7||
|3: Sep. 14||
|4: Sep. 21||
|5: Sep. 28||
|6: Oct. 5||
|7: Oct. 12||
|8: Oct. 19||
|9: Oct. 26||
|10: Nov. 2||
|11: Nov. 9||
|12: Nov. 16||
|13: Nov. 23||
|14: Nov. 30||
|15: Dec. 7||
|Finals Week: Wednesday Dec. 14||