  Performing optimal Bayesian design for discriminating between competing
models is computationally intensive as it involves estimating posterior model
probabilities for thousands of simulated datasets. This issue is compounded
further when the likelihood functions for the rival models are computationally
expensive. A new approach using supervised classification methods is developed
to perform Bayesian optimal model discrimination design. This approach requires
considerably fewer simulations from the candidate models than previous
approaches using approximate Bayesian computation. Further, it is easy to
assess the performance of the optimal design through the misclassification
error rate. The approach is particularly useful in the presence of models with
intractable likelihoods but can also provide computational advantages when the
likelihoods are manageable.
