Bayesian methods are becoming increasingly popular in the academic and practitioner communities because of the recent development of techniques like Markov chain Monte Carlo (MCMC) simulation. The Bayesian paradigm is an attempt to utilize all available information in decision-making. Prior knowledge coming from experience, expert judgment, or previously collected data is used with current data to characterize the current state of knowledge. These methods allow the use of models of complex physical phenomena that were previously too difficult to estimate. Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models of behavior that can be estimated with limited amounts of data. For a more complete discussion of the scope of Bayesian inference, refer to: Bayesian Research

• Dr. Sujit Ghosh
  
• Dr. Subhashis Ghosal
  
• Dr. Lexin Li
  
• Dr. John Monahan
  
• Dr. Brian Reich
  
• Dr. Hao (Helen) Zhang

The Bayesian Statistics Working Group is organized by Dr. Ghosh. The group meets periodically during the academic year to discuss and work on both applied and theoretical problems related to Bayesian inference. Meetings are informal, and participation by interested faculty and students is encouraged.

For up to date information about the activities of the Bayesian Statistics Working Group, refer to: Bayesian Statistics Working Group Website.

• IE712 Bayesian Decision Analysis for Engineers and Managers
  
• ST722 Decision Analytic Modeling
  
• ST740 Bayesian Inference
  
• ST810K Bayesian Nonparametrics
  

Starting spring 2004, several lectures on Bayesian estimation have been added to the ST422 syllabus.