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Cowell, R.; Lauritzen, S. L.; Mortera, J. (2009)
Publisher: Faculty of Actuarial Science & Insurance, City University London
Languages: English
Types: Article
Subjects: QA, QH426, HG
This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example.
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    • Butler, J. M. (2005). Forensic DNA typing. Elsevier, USA.
    • Clayton, T. M., Hill, S. M., Denton, L. A., Watson, S. K., and Urquhart, A. J. (2004). Primer binding site mutations a ecting the typing of STR loci contained within the AMPFl STRr SGM PlusT M kit. Forensic Science International, 139, 255{9.
    • Cowell, R. G. (2009). Validation of an STR peak area model. Forensic Science International: Genetics, 3, (3), 193{9.
    • Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J. (1999). Probabilistic Networks and Expert Systems. Springer, New York.
    • Cowell, R. G., Lauritzen, S. L., and Mortera, J. (2006). MAIES: A tool for DNA mixture analysis. In Proceedings of the 22nd Conference on Uncertainty in Arti cial Intelligence, (ed. R. Dechter and T. Richardson), pp. 90{7. Morgan Kaufmann Publishers, San Francisco.
    • Cowell, R. G., Lauritzen, S. L., and Mortera, J. (2007a). A gamma Bayesian network for DNA mixture analyses. Bayesian Analysis, 2, 333{48.
    • December 1995.
    • November 1996.
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