Title: Pushing the boundaries for forensic DNA interpretation
Authors: Therese Graversen - University of Copenhagen (Denmark) [presenting]
Abstract: Statistical interpretation of DNA from forensic evidence in crime cases may be computationally extremely demanding if the sample contains DNA from many people. While the forensic and statistical interpretation of the DNA sample concerns the DNA profiles of the people contributing to the sample, we may only observe the mixed signal of DNA components in the sample. The large discrete state space of the DNA profiles is the common root cause of a high computational complexity for all of the statistical models used for DNA interpretation in casework. The computational approach that we will show relies on well-established algorithms for computations in graphical models, one immediate advantage being that implementations are available in standard software. All computations are exact while still efficient enough to allow the interpretation of the more complex DNA samples occurring in practical casework. Many of the ideas are more generally applicable. One such example is to compute the likelihood in a model with discrete latent variables.