This paper shows how Bayesian Networks can be used to create models for discrete data from contingency tables. The advantage is that the models are created relatively automatically using existing software. The models provide representations that approximately preserve the joint relationships of variables and are easy to apply. The models allow imputation for missing data in contingency tables and for the creation of discrete, synthetic microdata satisfying analytic constraints.
