用相似度指数评价笔迹证据的似然比

Jȩdrzej Wydra, Szymon Matuszewski
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摘要

以前评估笔迹检查证据的方法通常与如何进行这些检查的重新定义有关。本文提出了一种与现行笔迹鉴定方案完全兼容的笔迹证据评估的似然比方法。该方法侧重于笔迹样本之间的相似性,使用Jaccard指数从通常的法医笔迹比较结果中量化。似然比的分子是给定相似性类别的概率,假设一个给定的人写了被质疑的样本。分母是同一类相似性的概率,假设一个随机选择的人写被质疑的样本。量化分子的相似性分布是通过对参考笔迹的比较得出的。为了计算分母,我们建议开发与特定法医场景相关的相似性分布。在概念验证研究中,我们开发了模拟场景的分布。
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Likelihood ratio to evaluate handwriting evidence using similarity index
Previous methods to evaluate evidence from handwriting examinations were usually associated with a redefinition of how these examinations are to be made. Here we propose the likelihood ratio method for handwriting evidence evaluation which is fully compatible with the current handwriting examination protocols. The method is focused on the similarity between handwriting samples, quantified using Jaccard index from results of a usual forensic handwriting comparison. The numerator of the likelihood ratio is the probability of a given class of similarity, assuming that a given person wrote the questioned sample. The denominator is the probability of the same class of similarity, assuming that a randomly selected person wrote questioned sample. The similarity distribution to quantify the numerator is derived from comparisons across reference handwritings. To calculate the denominator we propose to develop similarity distributions relevant for particular forensic scenarios. In the proof-of-a-concept study, we developed the distribution for the simulation scenario.
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How the work being done on statistical fingerprint models provides the basis for a much broader and greater impact affecting many areas within the criminal justice system Misuse of statistical method results in highly biased interpretation of forensic evidence in Likelihood ratios for categorical count data with applications in digital forensics Likelihood ratio to evaluate handwriting evidence using similarity index Interview with Professor Colin Aitken
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