ML Anthology

Independence of Causal Influence and Clique Tree Propagation

Nevin Lianwen Zhang, Li Yan
UAI 1997 pp. 481-488
doi:10.1016/S0888-613X(98)10014-2 /uai/1997/zhang1997uai-independence/

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.

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Cite

Text

Zhang and Yan. "Independence of Causal Influence and Clique Tree Propagation." Conference on Uncertainty in Artificial Intelligence, 1997. doi:10.1016/S0888-613X(98)10014-2

Markdown

[Zhang and Yan. "Independence of Causal Influence and Clique Tree Propagation." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/zhang1997uai-independence/) doi:10.1016/S0888-613X(98)10014-2

BibTeX

@inproceedings{zhang1997uai-independence,
  title     = {{Independence of Causal Influence and Clique Tree Propagation}},
  author    = {Zhang, Nevin Lianwen and Yan, Li},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1997},
  pages     = {481-488},
  doi       = {10.1016/S0888-613X(98)10014-2},
  url       = {https://mlanthology.org/uai/1997/zhang1997uai-independence/}
}