Inference Networks and the Evaluation of Evidence: Alternative Analyses

2013/01/23 by David A. Schum, Schum, David A.
Computer Science · Mathematics · #AI-based Problem Solving and Planning #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #cs.AI #stat.AP

paper · pdf · doi:10.48550/arxiv.1301.6737

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

arxiv created 2013/01/23 · openalex publication_date 2013/01/23 · arxiv updated 2013/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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