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The opportunity prior: a proof-based prior for criminal cases
Law, Probability and Risk ( IF 0.7 ) Pub Date : 2019-05-20 , DOI: 10.1093/lpr/mgz007
Norman Fenton 1 , David Lagnado 2 , Christian Dahlman 3 , Martin Neil 1
Affiliation  

One of the greatest challenges to the use of probabilistic reasoning in the assessment of criminal evidence is the ‘problem of the prior’, i.e. the difficulty in establishing an acceptable prior probability of guilt. Even strong supporters of a Bayesian approach have often preferred to ignore priors and focus on the likelihood ratio (LR) of the evidence. But to calculate if the probability of guilt, given the evidence reaches the probability required for conviction (the standard of proof), the LR has to be combined with a prior. In this article, we propose a solution to the ‘problem of the prior’: the defendant shall be treated as a member of the set of ‘possible perpetrators’ defined as the people who had the same or better opportunity as the defendant to commit the crime. For this purpose, we introduce the concept of an ‘extended crime scene’. The number of people who had the same or better opportunity as the defendant is the number of people who were just as close or closer to the crime scene, in time and space. We demonstrate how the opportunity prior is incorporated into a generic Bayesian network model that allows us to integrate other evidence about the case. (Less)

中文翻译:

机会先验:基于证据的刑事案件先验

在评估刑事证据时使用概率推理的最大挑战之一是“先验问题”,即难以确定可接受的有罪先验概率。即使是贝叶斯方法的坚定支持者,也常常倾向于忽略先验而专注于证据的似然比 (LR)。但是要计算是否有罪的概率,给定证据达到定罪所需的概率(证明标准),LR 必须与先验相结合。在这篇文章中,我们提出了“先验问题”的解决方案:被告应被视为“可能的肇事者”组的成员,这些人被定义为与被告有相同或更好的机会实施犯罪的人。犯罪。为此,我们引入了“扩展犯罪现场”的概念。与被告拥有相同或更好机会的人数是在时间和空间上与犯罪现场一样接近或接近的人数。我们展示了如何将机会先验纳入通用贝叶斯网络模型中,该模型使我们能够整合有关案例的其他证据。(较少的)
更新日期:2019-05-20
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