Measuring Heterogeneity in Forensic Databases Using Hierarchical Bayes Models

Kathryn Roeder, Michael Escobar, Joseph B. Kadane and Ivan Balazs

Abstract:

DNA fingerprint profiles, as currently defined, do not uniquely identify individuals. For criminal cases involving DNA evidence, forensic scientists evaluate the conditional probability that an unknown (but distinct) individual matches the crime sample, given that the defendant matches. Estimates of the conditional probability of observing matching profiles are based on reference populations maintained by forensic testing laboratories. Each of these databases is heterogeneous, being composed of subpopulations of different heritages. This heterogeneity has an impact on the weight of the evidence. A hierarchical Bayes model is formulated that incorporates the key physical characteristics inherent in these data. Using Markov chain Monte Carlo sampling, levels of heterogeneity are estimated for three major ethnic groups in the database of Lifecodes Corporation.

Keywords: DNA fingerprint, Hardy-Weinberg equilibrium, Gibbs Sampling, Population Heterogeneity

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