Bayesian Optimum Design Criterion for Multi Models Discrimination

The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an extension of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where the weights are corresponding probabilities of models to let them be true. We consider these probabilities coming from a Poisson distribution.