Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated data sets. 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.
Supplementary information: The online version contains supplementary material available at 10.1007/s11222-022-10078-2.
Keywords: Approximate Bayesian computation; Bayesian model selection; Classification and regression tree; Continuous-time Markov process; Random forest; Simulation-based Bayesian experimental design.
© The Author(s) 2022.