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CE_27T Wed, 8/12/2015, 8:00 AM - 9:45 AM S-Grand Ballroom A
Introducing the SAS BCHOICE Procedure for Bayesian Choice Models (ADDED FEE) — Professional Development Computer Technology Workshop
ASA , SAS
Discrete choice models (DCMs) are widely popular in marketing research and related areas, where it is important to model the underlying process a consumer uses to choose products or reach decisions when faced with multiple alternatives. The rising popularity of DCMs in the past decades coincides with the growing presence in this area of Bayesian approaches, which offer modeling and computational conveniences that are otherwise difficult to obtain. This tutorial introduces the BCHOICE procedure in SAS/STAT® 13.1, which is designed to perform Bayesian analysis for discrete choice models. The BCHOICE procedure supports all three major choice models: logit, nested logit, and probit. Models can be extended to include random effects to estimate individual-level parameters, better enabling you to infer heterogeneity in product preferences and price sensitivity. The BCHOICE procedure obtains samples from posterior distributions and produces summary and diagnostic statistics. It provides a CLASS statement to handle categorical variables and uses parallel processing to ensure fast sampling. This tutorial illustrates important features of the BCHOICE procedure and shows how to use it for estimation, inference, and prediction through various examples.
Instructor(s): Amy Shi, SAS Institute




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