Graphical models and likelihood ratios can be used by forensic scientists to compare support given by evidence to propositions put forward by competing parties during court proceedings. Such models can also be used to evaluate support for activity-level propositions, i.e. propositions that refer to the nature of activities associated with evidence and how this evidence came to be at a crime scene. Graphical methods can be used to show explicitly different scenarios that might explain the evidence in a case and to distinguish between evidence requiring evaluation by a jury and quantifiable evidence from the crime scene. Such visual representations can be helpful for forensic practitioners, the police and lawyers who may need to assess the value that different pieces of evidence make to their arguments in a case. In this paper we demonstrate for the first time how chain event graphs can be applied to a criminal case involving drug trafficking. We show how different types of evidence (i.e. expert judgement and data collected from a crime scene) can be combined using a chain event graph and show how the hierarchical model deriving from the graph can be used to evaluate the degree of support for different activity-level propositions in the case. We also develop a modification of the standard chain event graph to simplify their use in forensic applications.

翻译：法医学者可使用图形模型和似然比来比较证据对法庭诉讼中对立双方所提命题的支持程度。此类模型还可用于评估对活动层面命题的支持度，即涉及证据相关活动性质以及证据如何出现在犯罪现场的命题。图形方法可明确展示案件中可能解释证据的不同场景，并区分需要陪审团评估的证据与犯罪现场可量化的证据。这种可视化呈现对可能需要评估不同证据对其案件论证价值的法医从业者、警察和律师具有辅助作用。本文首次展示如何将链条事件图应用于涉及毒品贩运的刑事案件。我们演示了如何利用链条事件图整合不同类型证据（即专家判断与犯罪现场采集数据），并阐明如何运用源自图形的层次化模型评估案件中不同活动层面命题的支持程度。同时，我们改进了标准链条事件图以简化其在法医学应用中的使用流程。