Uncertainty in Artificial Intelligence
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Refinement and Coarsening of Bayesian Networks
Kuo-Chu Chang, Robert Fung
In almost all situation assessment problems, it is useful to dynamically contract and expand the states under consideration as assessment proceeds. Contraction is most often used to combine similar events or low probability events together in order to reduce computation. Expansion is most often used to make distinctions of interest which have significant probability in order to improve the quality of the assessment. Although other uncertainty calculi, notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there has not yet been any approach of refining and coarsening state spaces for the Bayesian Network technology. This paper presents two operations for refining and coarsening the state space in Bayesian Networks. We also discuss their practical implications for knowledge acquisition.
Keywords: null
Pages: 435-445
PS Link:
PDF Link: /papers/90/p435-chang.pdf
AUTHOR = "Kuo-Chu Chang and Robert Fung",
TITLE = "Refinement and Coarsening of Bayesian Networks",
BOOKTITLE = "Uncertainty in Artificial Intelligence 6 Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1990",
PAGES = "435--445"

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