1. A method for determining a distribution of an unobserved class for a classifier, the unobserved class having no known training data, comprising a processor for performing the steps of the method, comprising the steps of: determining, for each known class, known distribution using known training data; determining sufficient statistics of the distribution of the unobserved class from the known distributions and the training data associated with each known class: determining if the known training data and the known distributions are bounded, and if true: updating parameters of the distribution of the unobserved class from the sufficient statistics; else updating the parameters of the distribution of the unobserved class from sufficient statistics and a priori probability distributions that specify the distributions of the parameters.