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Probabilistic Expert Systems for Forensic Inference from Genetic Markers

Author

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  • A. P. DAWID
  • J. MORTERA
  • V. L. PASCALI
  • D. VAN BOXEL

Abstract

We present a number of real and fictitious examples in illustration of a new approach to analysing complex cases of forensic identification inference. This is effected by careful restructuring of the relevant pedigrees as a Probabilistic Expert System. Existing software can then be used to perform the required inferential calculations. Specific complications which are readily handled by this approach include missing data on one or more relevant individuals, and genetic mutation. The method is particularly valuable for disputed paternity cases, but applies also to certain criminal cases.

Suggested Citation

  • A. P. Dawid & J. Mortera & V. L. Pascali & D. Van Boxel, 2002. "Probabilistic Expert Systems for Forensic Inference from Genetic Markers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(4), pages 577-595, December.
    • Handle: RePEc:bla:scjsta:v:29:y:2002:i:4:p:577-595
    DOI: 10.1111/1467-9469.00307
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    Cited by:

    1. Fabio Corradi & Federico Ricciardi, 2013. "Evaluation of kinship identification systems based on short tandem repeat DNA profiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 649-668, November.
    2. Sheehan, Nuala A. & Bartlett, Mark & Cussens, James, 2014. "Improved maximum likelihood reconstruction of complex multi-generational pedigrees," Theoretical Population Biology, Elsevier, vol. 97(C), pages 11-19.
    3. Cowell, Robert G., 2009. "Efficient maximum likelihood pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 76(4), pages 285-291.
    4. Marco Di Zio & Mauro Scanu & Lucia Coppola & Orietta Luzi & Alessandra Ponti, 2004. "Bayesian networks for imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 309-322, May.

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