Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand; Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
Department of Forensic Science, College of Criminal Justice, Sam Houston State University, Huntsville, TX 77340, United States.
Sci Justice. 2022 Sep;62(5):540-546. doi: 10.1016/j.scijus.2022.08.002. Epub 2022 Aug 6.
There is a general reluctance to use conditioning profiles when forming propositions for cases where the evidence is a DNA mixture. However, the use of conditioning profiles improves the ability to differentiate true from false donors. There are at least four situations where this decision making is at its most difficult. These are:Rigorous mathematical treatment, given by Slooten and others, appears to offer strong guidance for these situations. This treatment assumes that the prior probabilities for conditioning, or not conditioning, on any individual are not extreme. It is when these prior probabilities appear ambiguous that the decision to condition or not can appear to be problematic. This is often the situation found in casework. In this paper we attempt to show that such situations may benefit most from following such guidance. A lower bound on the Bayes factor can be obtained by finding the highest LR that includes the POI and dividing by the highest LR that does not include the POI. These two highest LRs may be found with and without the disputed conditioning profile. The resultant lower bound is on the BF for the inclusion of the POI without directly assuming the disputed conditioning profile. Adopting this approach would both minimize adventitious inclusions and approximate an exhaustive set of propositions.
在形成涉及 DNA 混合证据的案例的命题时，人们普遍不愿意使用条件分布。然而，使用条件分布可以提高区分真实供体和虚假供体的能力。至少有四种情况下，这种决策最为困难。这些情况是：斯鲁滕等人给出的严格的数学处理方法似乎为这些情况提供了强有力的指导。这种处理方法假设，对于任何个体进行条件化或不条件化的先验概率都不是极端的。只有在先验概率看起来模棱两可时，是否进行条件化的决策才会显得有问题。这种情况在案例工作中经常出现。在本文中，我们试图表明，这种情况可能最受益于遵循这种指导。通过找到包含 POI 的最高似然比，并将其除以不包含 POI 的最高似然比，可以得到贝叶斯因子的下限。这两个最高似然比可以在有和没有争议的条件分布的情况下找到。得到的下限是在不直接假设争议的条件分布的情况下包含 POI 的 BF。采用这种方法既可以最大限度地减少偶然包含，又可以近似于一组详尽的命题。
