Modifying Bayesian Networks by Probability Constraints

This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We propose to solve this problem by extending IPFP (iterative proportional fitting procedure) to probability distributions represented by Bayesian networks. The resulting algorithm E-IPFP is fur-ther developed to D-IPFP, which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems. Limited analysis is provided, including the con-vergence proofs of the two algorithms. Computer experiments were conducted to validate the algo-rithms. The results are consistent with the theo-retical analysis.