逻辑斯谛回归校准和融合：将得分转换为似然比
Tutorial on logistic-regression calibration and fusion: Converting a score to a likelihood ratio
摘要：
逻辑斯谛回归校准和融合是计算法医似然比的潜在步骤。本文提供了一个教程逻辑斯谛回归校准和融合在一个实际的概念水平，最小的数学复杂性。分数是对数似然比，因为它表示一对样本的相似程度，同时考虑到它们相对于相关群体模型的典型性。较高值的分数比较低值的分数为相同来源假设提供了更多的支持，而不是不同来源假设;然而，分数的绝对值不能解释为对数似然比。逻辑斯谛回归校准是用于将分数转换为对数似然比的过程，而逻辑斯谛回归融合是用于将来自多个法医比较系统的平行分数集转换为对数似然比的过程。逻辑斯谛回归校准和融合被开发用于自动说话人识别，并且在法庭语音比较中很受欢迎。它们也可以应用于法医学的其他分支，提供了一个指纹/指纹的例子。
英文摘要：
Logistic-regression calibration and fusion are potential steps in the calculation of forensic likelihood ratios. The present paper provides a tutorial on logistic-regression calibration and fusion at a practical conceptual level with minimal mathematical complexity. A score is log-likelihood-ratio like in that it indicates the degree of similarity of a pair of samples while taking into consideration their typicality with respect to a model of the relevant population. A higher-valued score provides more support for the same-origin hypothesis over the different-origin hypothesis than does a lower-valued score; however, the absolute values of scores are not interpretable as log likelihood ratios. Logistic-regression calibration is a procedure for converting scores to log likelihood ratios, and logistic-regression fusion is a procedure for converting parallel sets of scores from multiple forensic-comparison systems to log likelihood ratios. Logistic-regression calibration and fusion were developed for automatic speaker recognition and are popular in forensic voice comparison. They can also be applied in other branches of forensic science, a fingerprint/fingermark example is provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
