Inference Networks and the Evaluation of Evidence: Alternative Analyses
This work addresses the challenge of evaluating evidence in legal contexts using inference networks, but it appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper tackles the problem of analyzing probabilistic inference networks by presenting three complementary methods for generating and combining probabilities, and illustrates their application in analyzing evidence from a celebrated American law case.
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.