Uncertainty in Artificial Intelligence
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Propagation using Chain Event Graphs
Peter Thwaites, Jim Smith, Robert Cowell
A Chain Event Graph (CEG) is a graphial model which designed to embody conditional independencies in problems whose state spaces are highly asymmetric and do not admit a natural product structure. In this paer we present a probability propagation algorithm which uses the topology of the CEG to build a transporter CEG. Intriungly,the transporter CEG is directly analogous to the triangulated Bayesian Network (BN) in the more conventional junction tree propagation algorithms used with BNs. The propagation method uses factorization formulae also analogous to (but different from) the ones using potentials on cliques and separators of the BN. It appears that the methods will be typically more efficient than the BN algorithms when applied to contexts where there is significant asymmetry present.
Pages: 546-553
PS Link:
PDF Link: /papers/08/p546-thwaites.pdf
AUTHOR = "Peter  Thwaites and Jim Smith and Robert Cowell",
TITLE = "Propagation using Chain Event Graphs",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "546--553"

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