The theory of comparative propensity, championed by the late Mike Redmayne, has been an influential theory underpinning normative models of the probative value of evidence of previous convictions in criminal trials. It purports to generalize an approximate probative value by means of a Bayesian model in which the likelihood of an innocent person having a criminal record is calculated by reference to general population statistics, and the hard evidence underpinning the prior probability is treated as unknown. The theory has been criticized on the ground that it fails to take account of bias against past offenders in the selection of cases for prosecution. This article analyses the model and these criticisms and concludes that both the model and the criticisms are flawed because they fail to address the evidence on which the prior odds are based. We find that, not only are such mathematical models unsound, but they can only be ‘repaired’ by making assumptions about the typical case which run counter to the legal presumption of innocence. Analysing the flaws in these models, however, does provide some insight into issues affecting the value of prior convictions evidence.

中文翻译：

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将隐性知识纳入先前定罪证据的贝叶斯模型中：比较倾向理论的一些现实检验

已故的迈克·雷德梅恩（Mike Redmayne）倡导的比较倾向理论一直是一种有影响力的理论，它支持了刑事审判中先前定罪证据的证明价值的规范模型。它旨在通过贝叶斯模型来概括一个近似的证明值，在贝叶斯模型中，参照一般人口统计数据来计算无辜者有犯罪记录的可能性，并且将支持先验概率的确凿证据视为未知。有人批评该理论，理由是它在选择起诉案件时没有考虑到对过去犯罪者的偏见。本文分析了该模型和这些批评，并得出结论，该模型和批评都存在缺陷，因为它们无法解决先验赔率所基于的证据。我们发现，不仅这种数学模型不健全，而且只能通过对与无罪的法律推定背道而驰的典型案例进行假设来“修复”。但是，分析这些模型中的缺陷确实可以洞悉影响先前定罪证据价值的问题。