Uncertainty in Artificial Intelligence
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Using Qualitative Relationships for Bounding Probability Distributions
Chao-Lin Liu, Michael Wellman
Abstract:
We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operations that are guaranteed to bound the distributions of interest in the desired direction. By evaluating incrementally improved approximate networks, our algorithm obtains monotonically tightening bounds that converge to exact distributions. For supermodular utility functions, the tightening bounds monotonically reduce the set of admissible decision alternatives as well.
Keywords: Qualitative probabilistic networks, approximation, state-space abstraction.
Pages: 346-353
PS Link: ftp://ftp.eecs.umich.edu/people/wellman/uai98liu-bound.ps
PDF Link: /papers/98/p346-liu.pdf
BibTex:
@INPROCEEDINGS{Liu98,
AUTHOR = "Chao-Lin Liu and Michael Wellman",
TITLE = "Using Qualitative Relationships for Bounding Probability Distributions",
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 = "346--353"
}


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