  The problem of categorical data analysis in high dimensions is considered. A
discussion of the fundamental difficulties of probability modeling is provided,
and a solution to the derivation of high dimensional probability distributions
based on Bayesian learning of clique tree decomposition is presented. The main
contributions of this paper are an automated determination of the optimal
clique tree structure for probability modeling, the resulting derived
probability distribution, and a corresponding unified approach to clustering
and anomaly detection based on the probability distribution.
