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Bayesian Networks Representations, Generalized Imputation, and Synthetic Micro-data satisfying Analytic Constraints

Yves Thibaudeau and William E. Winkler



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.


Source: U.S. Census Bureau, Statistical Research Division

Created: 21-NOV-2002

Source: U.S. Census Bureau | Statistical Research Division | (301) 763-3215 (or |   Last Revised: October 08, 2010