  This report works out the details of a closed-form, fully Bayesian,
multiclass, openset, generative pattern classifier using multivariate Gaussian
likelihoods, with conjugate priors. The generative model has a common
within-class covariance, which is proportional to the between-class covariance
in the conjugate prior. The scalar proportionality constant is the only plugin
parameter. All other model parameters are intergated out in closed form. An
expression is given for the model evidence, which can be used to make plugin
estimates for the proportionality constant. Pattern recognition is done via the
predictive likeihoods of classes for which training data is available, as well
as a predicitve likelihood for any as yet unseen class.
