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Jill Dando Institute of Crime Science

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Representing and solving complex DNA identification cases using Bayesian networks

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.

Speaker

Philip Dawid is Professor of Statistics at Cambridge University, having been Pearson Professor of Statistics at University College London from 1989 to 2007. He is Chartered Statistician and Fellow of the Royal Statistical Society, which has awarded him Guy Medals in Bronze and Silver; elected Fellow of the Institute of Mathematical Statistics; elected Member of the International Statistical Institute; and a Member of the Organising Committee for the Valencia International Meetings on Bayesian Statistics. He has served as Editor of the Journal of the Royal Statistical Society (Series B) and of Biometrika, and is currently an Editor of Bayesian Analysis. He was President of the International Society for Bayesian Analysis for the year 2000.