Bayesian D-Optimality

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Design of Experiments Guide • Choice Designs • Overview of Choice Designs • Bayesian D-Optimality

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Bayesian D-Optimality

Because discrete choice models are nonlinear in their parameters, the efficiency of a choice design depends on the unknown parameters. The Choice Design platform uses a Bayesian approach, optimizing the design over a prior distribution of likely parameter values that you specify. The Bayesian D-optimality criterion is the expected logarithm of the determinant of the information matrix, taken with respect to the prior distribution. The Choice Design platform maximizes this expectation with respect to the prior probability distribution. For details, see Bayesian D-Optimality and Design Construction and Kessels et al. (2011).

You can also generate the following types of designs:

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Utility-neutral designs - In a utility-neutral design, all choices within a choice set are equally probable. The prior mean is set to 0.

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Local D-optimal designs - A local D-optimal design takes into account the prior on the mean, but does not include any information from a prior covariance matrix.

For more information about utility neutral and local D-optimal designs, see Utility-Neutral and Local D-Optimal Designs.

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Help created on 7/12/2018