1. A system for automatic probabilistic pattern tracking comprising: automatically learning a set of exemplars from at least one set of training data; clustering the exemplars into more than one cluster of exemplars, with each cluster having a representative cluster at a cluster center; generating an observation likelihood function for each exemplar cluster based on a computed distance between exemplars each cluster; providing the exemplar clusters, observation likelihood functions, and target data to a probabilistic tracking function; and probabilistically tracking at least one pattern in the target data by using the exemplar clusters, observation likelihood functions, and target data to predict at least one target state.