1. A computer-implemented method for anomaly detection, the method comprising: extracting a plurality of features from a set of data for anomaly detection; applying a Bayesian belief network (BBN) model to the data set, including for each particular feature of the plurality of features, performing an estimate of the particular feature using known observed values of remaining features of the plurality of features to generate a posterior probability for the particular 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.