论文标题
普遍的基准因素：贝叶斯因子的替代因素，用于法医识别来源问题
Generalized fiducial factor: an alternative to the Bayes factor for forensic identification of source problems
论文作者
论文摘要
法医确定来源问题的一种表述是确定痕量证据的来源，例如，在犯罪嫌疑人中发现的玻璃碎片。该科学的当前状态是计算贝叶斯因子（BF），以比较两个竞争命题下的痕量证据的边际分布，即是否起源于特定来源的未知来源证据。这种方法的显而易见的问题是能够定制先前的分布（放置在痕量证据的统计模型的特征/参数上），以支持辩护或起诉，这进一步使痕量证据的典型测量数量的典型测量数量更为复杂，即先前选择/规范通常会对BF的价值产生强大的影响。为了解决此先前规范和选择的这个问题，我们在广义基金推理（GFI）的框架内开发了BF的替代方案，即我们称为{\ em广义基准因子}（GFF）。此外，我们从经验上证明了合成和真实的荷兰法医学研究所（NFI）案例数据，BF的缺陷以及经典/经典主义者的可能性比率（LR）方法。
One formulation of forensic identification of source problems is to determine the source of trace evidence, for instance, glass fragments found on a suspect for a crime. The current state of the science is to compute a Bayes factor (BF) comparing the marginal distribution of measurements of trace evidence under two competing propositions for whether or not the unknown source evidence originated from a specific source. The obvious problem with such an approach is the ability to tailor the prior distributions (placed on the features/parameters of the statistical model for the measurements of trace evidence) in favor of the defense or prosecution, which is further complicated by the fact that the typical number of measurements of trace evidence is typically sufficiently small that prior choice/specification has a strong influence on the value of the BF. To remedy this problem of prior specification and choice, we develop an alternative to the BF, within the framework of generalized fiducial inference (GFI), that we term a {\em generalized fiducial factor} (GFF). Furthermore, we demonstrate empirically, on the synthetic and real Netherlands Forensic Institute (NFI) casework data, deficiencies in the BF and classical/frequentist likelihood ratio (LR) approaches.
