Teaching Bayesian Statistics to Marketing and Business Students
Greg M. Allenby
Ohio State University (OSU) - Department of Marketing and Logistics
Peter E. Rossi
University of California, Los Angeles (UCLA) - Anderson School of Management
February 1, 2008
Fisher College of Business Working Series
Chicago GSB Research Paper Series
We discuss our experiences teaching Bayesian Statistics to students in doctoral programs in business. These students often have weak backgrounds in mathematical statistics and a predisposition against likelihood-based methods stemming from prior exposure to econometrics. This can be overcome by an intense course which emphasizes the value of the Bayesian approach to solving non-trivial problems. The success of our course is primarily due to the emphasis on statistical computing. This is facilitated by our R package, bayesm, which provides efficient implementation of advanced methods and models.
Number of Pages in PDF File: 8
Keywords: bayesm package, hierarchical models, posterior inference, R software
JEL Classification: C11, M1, M3working papers series
Date posted: February 14, 2008 ; Last revised: February 19, 2008
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