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This paper examines whether Bayesian networks are expressive enough to model reasoning with evidence in legal cases. Bayesian networks can represent many familiar patterns of evidential reasoning, including inferences from evidence to hypotheses, cumulative support from multiple items of evidence, and chains of inferences linking intermediate hypotheses to ultimate guilt. But other evidential inferences common in legal cases are more difficult to model. Focusing on a real criminal case, the paper distinguishes between identity- and event-level inferences. Event-level inferences show why certain actions amount to guilty conduct, while identity-level inferences link the defendant to those actions. The challenge for Bayesian network models of legal evidence is to represent how identity- and event-level inferences combine and reinforce one another. Meeting this challenge requires extending Bayesian networks beyond a purely propositional language.
