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Bayesian approach to LR assessment in case of rare type match

机译：罕见类型匹配的贝叶斯方法进行LR评估

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The likelihood ratio (LR) is largely used to evaluate the relative weight of forensic data regarding two hypotheses, and for its assessment, Bayesian methods are widespread in the forensic field. However, the Bayesian 'recipe' for the LR presented in most of the literature consists of plugging-in Bayesian estimates of the involved nuisance parameters into a frequentist-defined LR: frequentist and Bayesian methods are thus mixed, giving rise to solutions obtained by hybrid reasoning. This paper provides the derivation of a proper Bayesian approach to assess LRs for the 'rare type match problem', the situation in which the expert wants to evaluate a match between the DNA profile of a suspect and that of a trace from the crime scene, and this profile has never been observed before in the database of reference. LR assessment using the two most popular Bayesian models (beta-binomial and Dirichlet-multinomial) is discussed and compared with corresponding plug-in versions.
机译：似然比（LR）主要用于评估关于两个假设的法证数据的相对权重，并且为了评估它，贝叶斯方法在法证领域很普遍。但是，大多数文献中提出的有关LR的贝叶斯“食谱”是将涉及的扰动参数的贝叶斯估计值插入到一个由常客定义的LR中：因此，常客和贝叶斯方法混合在一起，从而产生了通过混合获得的解推理。本文提供了一种合适的贝叶斯方法来评估“稀有类型匹配问题”的LR，专家希望评估嫌疑人的DNA图谱与犯罪现场的痕迹图谱之间的匹配，而且此参考资料从未在参考数据库中被观察到。讨论了使用两种最流行的贝叶斯模型（β-二项式和Dirichlet-多项式）进行的LR评估，并将其与相应的插件版本进行了比较。

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