Practical Bayesian optimal design for discrete choice experiments
Seminar Room 1, Newton Institute
AbstractThe use of the world wide web for marketing has exploded recently. The web makes contacting customers and learning their preferences for potential products fast and affordable. Discrete choice experiments are a powerful tool for establishing the relative importance that the marketplace will put on the features of a new product. Since the underlying model for such experiments is nonlinear, it is necessary to provide some information about the model parameters in order to generate an efficient design. Bayesian methods provide just the formalism that is needed here. Unfortunately, the generation of optimal designs using Bayesian methods has been difficult because of the intensive computing required. This presentation describes some recent advances that make the computation of these designs feasible for practical web use. A case study will be presented for finding the preferences of users of statistical software for elements of the display diagnostic graphs.
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