Uncertainty in Artificial Intelligence
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Independence of Causal Influence and Clique Tree Propagation
Nevin Zhang, Li Yan
Abstract:
This paper explores the role of independence of causal influence (ICI) in Bayesian network inference. ICI allows one to factorize a conditional probability table into smaller pieces. We describe a method for exploiting the factorization in clique tree propagation (CTP) --- the state-of-the-art exact inference algorithm for Bayesian networks. We also present empirical results showing that the resulting algorithm is significantly more efficient than the combination of CTP and previous techniques for exploiting ICI.
Keywords: Bayesian networks, independence of causal influence (Causal independence), inferenc
Pages: 481-488
PS Link: file://ftp.cs.ust.hk/pub/lzhang/uai97yan.ps.gz
PDF Link: /papers/97/p481-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang97,
AUTHOR = "Nevin Zhang and Li Yan",
TITLE = "Independence of Causal Influence and Clique Tree Propagation",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "481--488"
}


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