638
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
Here is the full postscript text for this
technical report. It is 338284 bytes long.