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 further developed to D-IPFP, which reduces the
computational cost by decomposing a global EIPFP into a set of smaller local
E-IPFP problems. Limited analysis is provided, including the convergence proofs
of the two algorithms. Computer experiments were conducted to validate the
algorithms. The results are consistent with the theoretical analysis.
