Figure
1. Why you should attend the course. (Notice that the Bayesian analysis reveals many credible regression lines, for which the slopes and intercepts trade off, instead of just one "best" line.) 
Bayesian data analysis is rapidly replacing traditional methods because if provides richer inferences and without reference to illdefined p values. This workshop introduces modern Bayesian data analysis, starting with groundlevel concepts of probabilities and Bayes’ rule, and moving up to Bayesian hierarchical modeling applied to multiple regression and analysis of variance. Prerequisites: Only “rusty” familiarity with integrals; no linear algebra.
Tentative agenda:
Why go Bayesian? See Figure 1. But beyond that, sciences from astronomy to zoology are changing from 20thcentury nullhypothesis significance testing to Bayesian data analysis. Read more:
Who is the instructor? John
Kruschke is fivetime winner of
Teaching Excellence Recognition Awards from Indiana University, where
he is Professor of Psychological and Brain Sciences, and Adjunct
Professor of Statistics. He has written
an introductory textbook on
Bayesian data analysis; see also the
articles linked above. His research interests include models of
attention in learning, which he has developed in both connectionist
and Bayesian formalisms. He received the Troland Research Award from the
National Academy of Sciences.
Bringing a notebook computer?
You do not need to bring a notebook computer to the course. But you are invited to bring one, so that you can run the programs and see how their output corresponds with the presentation material. If you want to bring a notebook computer to the course, you must install the software listed below before arriving at the course, because there will not be time to do it during the tutorial.

This page URL: http://www.indiana.edu/~jkkteach/WorkshopICPSR2011.html