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    The a posteriori probability of the number of contributors when conditioned on an assumed contributor

    Grgicak, Catherine and Duffy, Ken R. and Lun, Desmond S. (2021) The a posteriori probability of the number of contributors when conditioned on an assumed contributor. Forensic Science International: Genetics, 54 (102563). ISSN 1878-0326

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    Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpre- tation. 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 Number of Contributors (NOC) to a sample have been developed, as have Bayesian methods that evaluate an A Posteriori Probability (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 the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the NOC given an electropherogram, by reporting on an implementation where the user can add assumed contrib- utors. NOCIt is a continuous system that incorporates models of peak height (including degradation and dif- ferential 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, we 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. 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, we show that this computation must not only use coincident models, but also coincident contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple contexts.

    Item Type: Article
    Keywords: Forensic DNA; Mixtures; DNA mixtures; Number of contributors;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15189
    Identification Number:
    Depositing User: Dr Ken Duffy
    Date Deposited: 06 Jan 2022 15:51
    Journal or Publication Title: Forensic Science International: Genetics
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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