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Many forensic genetics problems can be handled using structured systems
of discrete variables, for which Bayesian networks offer an appealing
practical modelling framework, and allow inferences to be computed by
probability propagation methods. However, when standard assumptions are
violated-for example, when allele frequencies are unknown, there is
identity by descent or the population is heterogeneous-dependence is
generated among founding genes, that makes exact calculation of
conditional probabilities by propagation methods less straightforward.
Here we illustrate different methodologies for assessing sensitivity to
assumptions about founders in forensic genetics problems. These include
constrained steepest descent, linear fractional programming and
representing dependence by structure. We illustrate these methods on
several forensic genetics examples involving criminal identification,
simple and complex disputed paternity and DNA mixtures
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