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Teaching Bayesian Statistics to Marketing and Business StudentsGreg M. AllenbyOhio State University (OSU) - Department of Marketing and Logistics Peter E. RossiUCLA-Anderson School of Management February 1, 2008 Fisher College of Business Working Series Chicago GSB Research Paper Series Abstract: 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, M3 working papers seriesDate posted: February 14, 2008 ; Last revised: February 19, 2008Suggested CitationContact Information
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