This paper studies expected performance and practical feasibility of the most commonly used classes of source-level likelihood-ratio (LR) systems when applied to a trace-reference comparison problem. The paper compares performance of these classes of LR systems (used to update prior odds) to each other and to the use of prior odds only, using strictly proper scoring rules as performance measures. It also explores practical feasibility of the classes of LR systems. The present analysis allows for a ranking of these classes of LR systems: from specific-source feature-based to common-source anchored or non-anchored score-based. A trade-off between performance and practical feasibility is observed, meaning that the best performing class of LR systems is the hardest to realise in practice, while the least performing class is the easiest to realise in practice. The other classes of LR systems are in between the two extremes. The one positive exception is a common-source feature-based LR system, with good performance and relatively low experimental demands. The paper also argues against the claim that some classes of LR systems should not be used, by showing that all systems have merit (when updating prior odds) over just using the prior odds (i.e. not using the LR system).

翻译：本文研究了在痕迹-参照物比对问题中，最常用的几类源级似然比系统的预期性能与实践可行性。文章以严格恰当评分规则为绩效指标，将这些似然比系统（用于更新先验概率）的性能相互比较，并与仅使用先验概率的情形进行对比，同时探讨了这些系统的实践可行性。本分析实现了对这些似然比系统的排序：从基于特定源特征的系统，到基于公共源锚定或非锚定得分的系统。性能与实践可行性之间存在权衡——性能最优的似然比系统在实际中最难实现，而性能最差的系统最易实现。其余系统则介于这两个极端之间。唯一的积极例外是基于公共源特征的似然比系统，其性能优良且实验要求相对较低。本文还通过论证所有系统（在更新先验概率时）均优于仅使用先验概率（即不使用似然比系统），驳斥了部分系统不应被使用的观点。