Modifying Bayesian Networks by Probability Constraints
Yun Peng, Zhongli Ding
Abstract:
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 EIPFP is further developed to DIPFP, which reduces the computational cost by decomposing a global EIPFP into a set of smaller local EIPFP 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.
Keywords:
Pages: 459466
PS Link:
PDF Link: /papers/05/p459peng.pdf
BibTex:
@INPROCEEDINGS{Peng05,
AUTHOR = "Yun Peng
and Zhongli Ding",
TITLE = "Modifying Bayesian Networks by Probability Constraints",
BOOKTITLE = "Proceedings of the TwentyFirst Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "459466"
}

