Uncertainty in Artificial Intelligence
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Irrelevance and Independence Relations in Quasi-Bayesian Networks
Fabio Cozman
Abstract:
This paper analyzes irrelevance and independence relations in graphical models associated with convex sets of probability distributions (called Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How can irrelevance/independence relations in Quasi-Bayesian networks be detected, enforced and exploited? This paper addresses these questions through Walley's definitions of irrelevance and independence. Novel algorithms and results are presented for inferences with the so-called natural extensions using fractional linear programming, and the properties of the so-called type-1 extensions are clarified through a new generalization of d-separation.
Keywords: Convex sets of probability, robust statistics, graphical models, Bayesian networks, d
Pages: 89-96
PS Link: http://www.cs.cmu.edu/~qbayes/QuasiBayesianNetworks/UAI98/uai98.ps.gz
PDF Link: /papers/98/p89-cozman.pdf
BibTex:
@INPROCEEDINGS{Cozman98,
AUTHOR = "Fabio Cozman ",
TITLE = "Irrelevance and Independence Relations in Quasi-Bayesian Networks",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "89--96"
}


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