Minimax Gaussian Bayes classifier from equal class errors

For two distinct nonsingular Gaussian class distributions, vary the class-zero prior continuously. The class-zero and class-one error probabilities of the corresponding Bayes classifier cross. At a crossing they are equal, making the classifier an equalizer rule, a minimax decision rule, and the associated prior a least favorable prior.