This study explores the power of modern probabilistic systems to assess full weights-of-evidence and to provide information on reasonable Number of Contributors (NOC) ranges across multiple contexts.
This paper extends previous work on NOCIt, a Bayesian method that determines an A Posteriori Probability (APP) on the Number of Contributors (NOC) given an electropherogram, by reporting on an implementation where the user can add assumed contributors. The results illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts. NOCIt is a continuous system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele frequencies in a reference population. When conditioned on a known contributor, the authors found that the mode of the APP distribution can shift to one greater when compared with the circumstance where no known contributor is assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture. Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpretation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational tools that make point estimates on the NOC to a sample have been developed, as have Bayesian methods that evaluate an APP distribution on the NOC. In cases where an overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the evidence is evaluated with an inadequate set of propositions. In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution and defense hypotheses, the authors show that this computation must not only use coincident models, but also coincident contextual information. (Published Abstract Provided)
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