Julia Mortera:
Probabilistic Expert Systems for Forensic Identification
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Problems of forensic identification from DNA profile evidence can
become extremel challenging, both logically and computationally, in
the presence of such complicating features as missing data on
individuals, mutation, mixed trace evidence, laboratory contamination
and artifacts as well as violations of standard assumptions about
founding genes. In recent years it has been shown how object-oriented
Bayesian networks can be used to represent and solve such problems.
This architecture proves particularly natural and useful for complex
forensic identification problems. I will describe a "construction set"
of fundamental networks, that can be pieced together, as required, to
represent and solve a wide variety of problems arising in forensic
genetics.
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 illustrate the use of the networks on published criminal casework
examples.