调和共源、特定源、基于特征与基于评分的似然比
Reconciling common source, specific source, feature based and score based likelihood ratios
AI总结：
本文通过似然比基本性质直接证明恰当纳入新信息可降低最优决策期望成本，调和了不同类型似然比的争论，指出其差异仅为处理信息不同，且评分可用于司法实践。
摘要：
我们证明，只要通过对假设概率进行恰当更新来纳入任意新信息，最优决策的期望成本就会降低（在未纳入新信息或纳入新信息不足的边界情形下，期望成本保持不变），从而可改善决策效果。此前已有研究提出过该结论的不同版本，但过往证明均依赖基于恰当评分规则的辅助构造。与之不同，我们仅利用似然比的基本性质，给出了一种直接且完全通用的证明方法。我们将所得结果应用于围绕基于评分/基于特征、共源/特定源似然比应用的相关争论，以此作出贡献。现有文献中这些方法常被表述为不同的“LR（似然比）系统”，我们则论证，这些方法间的差异仅在于所处理的信息不同，因此并不存在所谓不同的“LR系统”，区别仅在于处理的信息存在差异。尤其需要指出的是，尽管存在相反主张，评分完全可以应用于司法实践，我们通过DNA亲缘关系鉴定场景下的详尽示例对此进行了说明。
英文摘要：
We show that the incorporation of any new piece of information allows for improved decision making in the sense that the expected costs of an optimal decision decrease (or, in boundary cases where no or not enough new information is incorporated, stays the same) whenever this is done by the appropriate update of the probabilities of the hypotheses. Versions of this result have been stated before. However, previous proofs rely on auxiliary constructions with proper scoring rules. We, instead, offer a direct and completely general proof by considering elementary properties of likelihood ratios only. We apply our results to make a contribution to the debates about the use of score based/feature based and common/specific source likelihood ratios. In the literature these are often presented as different ``LR-systems''. We argue that the difference between these is simply a matter which information is processed. There is no therefore no such thing as different ``LR-systems'', there are only differences in the processed information. In particular, despite claims to the contrary, scores can very well be used in forensic practice and we illustrate this with an extensive example in DNA kinship context.
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。
