Bayesian methods are widely recognized for the unified approach that they offer to statistical analysis. Additionally, these methods are readily amenable to decision problems which are frequently found in Marketing problems. The growth of Bayesian methods in the last ten years is largely due to a new class of computational methods to solve previously intractable problems. These methods rely upon simulation techniques, particularly the use of the Markov Chain Monte Carlo (MCMC). These new techniques have allowed applied researchers and practitioners to focus more on solving new problems instead of the methodology. This has opened up new areas applied research to Bayesian methods. The focus of this course is applications of Bayesian statistical analysis to one of these areas, Marketing. We begin with a discussion of the Bayesian approach and follow this with the general linear model with special emphasis on hierarchical models. Another major focus is discrete choice modeling.
Lecture: 100min/wk and Recitation: 50min/wk