Title: Introduction to Bayesian Methods and Software for Data Analysis

Instructors:  Brad Carlin, University of Minnesota
              Laura Hatfield, Harvard Medical School

Course description: 

Hierarchical Bayes methods enable the combining of information from similar 
and independent experiments, yielding improved inference for both individual 
and shared model characteristics.  As a result of recent advances in computing 
and the consequent ability to evaluate complex models, Bayesian methods have 
increased in popularity in data analysis.  This course introduces hierarchical 
Bayes methods, demonstrates their usefulness in challenging applied settings, 
and shows how they can be implemented using modern Markov chain Monte Carlo
(MCMC) computational methods.  Related issues in Bayesian robustness, model 
assessment, and model selection and averaging are also discussed, including 
Bayes factors, predictive approaches, and penalized likelihood methods including 
DIC.  We also provide an introduction to and live demonstration of WinBUGS, 
the most general and widely-used Bayesian software package available to date.  
Use of the methods will be demonstrated in advanced high-dimensional model 
settings (e.g., nonlinear longitudinal modeling or clinical trial design and 
analysis), where the MCMC Bayesian approach often provides the only feasible 
alternative that incorporates all relevant model features.

Target Audience:  

Practicing statisticians and biostatisticians seeking to learn new
methods for analyzing complex datasets drawn from industry, agriculture,
biomedical science, public health, and other fields;

Statistical consultants looking to broaden their toolkit of
procedures for challenging datasets;

Educators  wanting to incorporate recent developments in Bayesian
methods into their courses on nonlinear models, longitudinal data
analysis, time series, survival analysis, spatial statistics, and so on;

Statistical researchers in application areas who need an introduction
to the benefit of Bayesian methods for hierarchical modeling;

Masters and Ph.D. students in statistics, biostatistics,
epidemiology, and related fields who recognize the need for advanced
modeling tools in their thesis research, but whose departments do not
offer formal course work on Bayesian methods and software.


Requirements for participation (e.g., any specific prior knowledge):

Short course participants should have an M.S. (or advanced undergraduate)
understanding of mathematical statistics at, say, the Hogg and Craig (1978)
level.  Basic familiarity with common statistical models (e.g., the linear
regression model) and computing will be assumed, but we will not assume any
significant previous exposure to Bayesian methods or Bayesian computing.
The course is generally aimed at students and practicing statisticians who
are intrigued by all the fuss about Bayes and Gibbs, but who may still mistrust
the approach as theoretically mysterious and practically cumbersome.

Also, students are invited to bring their own laptop computers to
the session, and to have the latest versions of WinBUGS and R already
installed on these computers. Both of these programs are freely
available from

http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml

and

http://www.r-project.org/

respectively.