Inference networks and the evaluation of evidence: alternative analyses

Authors:
David A. Schum
Affiliations:
School of Information Technology and Engineering, School of Law, George Mason University, Fairfax, VA
Venue:
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Year:
1999

Citing 3
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Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
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Management Science
A general algorithm for determining likelihood ratios in cascaded inference

A general algorithm for determining likelihood ratios in cascaded inference

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Abstract

Inference networks have a variety of important uses and are constructed by persons having quite different standpoints. Discussed in this paper are three different but complementary methods for generating and analyzing probabilistic inference networks. The first method, though over eighty years old, is very useful for knowledge representation in the task of constructing probabilistic arguments. It is also useful as a heuristic device in generating new forms of evidence. The other two methods are formally equivalent ways for combining probabilities in the analysis of inference networks. The use of these three methods is illustrated in an analysis of a mass of evidence in a celebrated American law case.