  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.
