Abstract
A Bayesian network is a knowledge representation technique for use in expert system development. The probabilistic knowledge encoded in a Bayesian network is a set of composite hypotheses expressed over the permutation of a set of variables (propositions). Ordering these composite hypotheses according to their a posteriori probabilities can be exponentially hard. This paper presents a qualitative reasoning approach which takes advantage of certain types of topological structures and probability distributions of a Bayesian network to derive the partial ordering of composite hypotheses. Such an approach offers an attractive alternative to reduce the computational complexity of deriving a partial ordering in which consistency is guaranteed.
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This work is supported in part by a grant to Queens College from the General Research Branch, National Institute of Health under grant No. RR-07064.
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Sy, B.K. Reasoning composite beliefs using a qualitative approach. Ann Math Artif Intell 4, 1–23 (1991). https://doi.org/10.1007/BF01531171
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DOI: https://doi.org/10.1007/BF01531171