Uncertainty in Artificial Intelligence
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Contextual Weak Independence in Bayesian Networks
Michael Wong, C. Butz
Abstract:
It is well-known that the notion of (strong) conditional independence (CI) is too restrictive to capture independencies that only hold in certain contexts. This kind of contextual independency, called context-strong independence (CSI), can be used to facilitate the acquisition, representation, and inference of probabilistic knowledge. In this paper, we suggest the use of contextual weak independence (CWI) in Bayesian networks. It should be emphasized that the notion of CWI is a more general form of contextual independence than CSI. Furthermore, if the contextual strong independence holds for all contexts, then the notion of CSI becomes strong CI. On the other hand, if the weak contextual independence holds for all contexts, then the notion of CWI becomes weak independence (WI) nwhich is a more general noncontextual independency than strong CI. More importantly, complete axiomatizations are studied for both the class of WI and the class of CI and WI together. Finally, the interesting property of WI being a necessary and sufficient condition for ensuring consistency in granular probabilistic networks is shown.
Keywords: Conditional Independence, Context-Specific Independence, Complete Axiomatization, Obj
Pages: 670-679
PS Link: http://www.cs.uregina.ca/~wong/papers/uai99.ps
PDF Link: /papers/99/p670-wong.pdf
BibTex:
@INPROCEEDINGS{Wong99,
AUTHOR = "Michael Wong and C. Butz",
TITLE = "Contextual Weak Independence in Bayesian Networks",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "670--679"
}


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