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Webinar: "Driver Analysis with Probabilistic Structural Equation Models"
February, 28 2011
Probabilistic Structural Equation Models (PSEM), based on machine-learned Bayesian networks, provide an efficient alternative to traditional Structural Equation Models (SEM). With BayesiaLab 5.0, PSEMs can be created through a series of semi-automatic clustering steps, which allow analysts to perform driver analysis extremely quickly, reducing research time from “months to minutes.” This webinar will demonstrate a complete workflow for a typical application in the field of marketing science, namely driver analysis of purchase intent and product optimization based on consumer survey data.
This topic is also covered in one of our recent white papers, Driver Analysis & Product Optimization, which you may want to review in preparation of the webinar.
The answers to the questions asked during that webinar are on the BayesiaLab's LinkedIn group.

