Bayesian Networks for Complex DNA mixture analysis
Abstract: We show how probabilistic expert systems can be used to analyse forensic identification problems involving DNA mixture traces using peak area 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. We will also present an extension of the Bayesian network for taking account artifacts such as allelic dropout, stutter bands and silent alleles when interpreting DNA profiles from a single and from a pair of mixture traces.
We illustrate the use of the network on a published criminal casework example.
This is joint work with Robert Cowell and Steffen Lauritzen
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