1. A computer-implemented method for anomaly detection, the method comprising: in response to a set of data for anomaly detection, applying a Bayesian belief network (BBN) model to the data set, including for each of a plurality of features of the BBN model, performing an estimate using known observed values associated with remaining features to generate a posterior probability for the corresponding feature; and performing a scoring operation using a predetermined scoring algorithm on posterior probabilities of all of the features to generate a similarity score, wherein the similarity score represents a degree to which a given event represented by the data set is novel relative to historical events represented by the BBN model.