  We extend the standard Bayesian multivariate Gaussian generative data
classifier by considering a generalization of the conjugate, normal-Wishart
prior distribution and by deriving the hyperparameters analytically via
evidence maximization. The behaviour of the optimal hyperparameters is explored
in the high-dimensional data regime. The classification accuracy of the
resulting generalized model is competitive with state-of-the art Bayesian
discriminant analysis methods, but without the usual computational burden of
cross-validation.
