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.

执行最佳贝叶斯设计以区分竞争模型是计算密集型的，因为它涉及估计数千个模拟数据集的后验模型概率。当竞争模型的似然函数计算量大时，这个问题会进一步复杂化。开发了一种使用监督分类方法的新方法来执行贝叶斯最优模型判别设计。与以前使用近似贝叶斯计算的方法相比，这种方法需要从候选模型进行的模拟要少得多。此外，通过误分类错误率很容易评估最优设计的性能。