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贝叶斯多元模型在动态特征案例中的病例评估。

Bayesian multivariate models for case assessment in dynamic signature cases.

机构信息

School of Criminal Justice, University of Lausanne, CH-1015 Lausanne Dorigny, Switzerland.

School of Criminal Justice, University of Lausanne, CH-1015 Lausanne Dorigny, Switzerland.

出版信息

Forensic Sci Int. 2021 Jan;318:110611. doi: 10.1016/j.forsciint.2020.110611. Epub 2020 Nov 22.

Abstract

Dynamic signatures are recordings of signatures made on digitizing devices such as tablet PCs. These handwritten signatures contain both dynamic and spatial information on every data point collected during the signature movement and can therefore be described in the form of multivariate data. The management of dynamic signatures represents a challenge for the forensic science community through its novelty and the volume of data available. Much as for static signatures, the authenticity of dynamic signatures may be doubted, which leads to a forensic examination of the unknown source signature. The Bayes' factor, as measure of evidential support, can be assigned with statistical models to discriminate between competing propositions. In this respect, the limitations of existing probabilistic solutions to deal with dynamic signature evidence is pointed out and explained in detail. In particular, the necessity to remove the independence assumption between questioned and reference material is emphasized.

摘要

动态签名是在数字化设备(如平板电脑)上制作签名的记录。这些手写签名包含签名运动过程中每个数据点的动态和空间信息,因此可以用多元数据的形式来描述。由于其新颖性和可用数据量,动态签名的管理对法医学界来说是一个挑战。与静态签名一样,动态签名的真实性可能受到怀疑,这就需要对来源不明的签名进行法医检验。贝叶斯因子作为证据支持的度量,可以通过统计模型来分配,以区分竞争的命题。在这方面,指出并详细解释了现有概率解决方案在处理动态签名证据方面的局限性。特别是强调了必须消除质疑材料和参考材料之间的独立性假设。

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