Probabilistic Expert Systems for Handling Artifacts in Complex DNA Mixtures
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This paper presents a coherent probabilistic framework for taking account
of allelic dropout, stutter bands and silent alleles when
interpreting short tandem repeat DNA profiles from a mixture sample using
peak size information. This information can be exploited to make
inferences regarding the genetic profiles of unknown contributors to the
mixture, or 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
criminal casework example.
This is joint work with R. G. Cowell and S. L. Lauritzen
This talk is part of the Statistics series.
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