A Bayesian network (BN) is a graphical model of uncertainty that is especially wellsuited to legal arguments. It enables us to visualise and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments there is no systematic, repeatable method for modelling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad-hoc, with little possibility for learning and process improvement. This paper directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid and Leucari on objected-oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built u
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