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Abstract
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Problems of forensic identification from DNA profile evidence can become extremely challenging, both logically and computationally, in the presence of such complicating features as missing data on individuals, mixed trace evidence, mutation, silent alleles, laboratory and handling errors, etc. etc. In recent years it has been shown how Bayesian networks can be used to represent and solve such problems.
"Object-oriented" Bayesian network systems, such as Hugin version 6, allow a network to contain repeated instances of other networks. This architecture proves particularly natural and useful for genetic problems, where there is repetition of such basic structures as Mendelian inheritance or mutation processes.
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. Some examples of their use will be provided.
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