Measuring heterogeneity in forensic databases using hierarchical Bayes models

• 成果类型: Article
• 署名作者: Roeder, K; Escobar, M; Kadane, JB; Balazs, I
• 署名单位: Carnegie Mellon University; University of Toronto; Carnegie Mellon University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/85.2.269
• 发表日期: 1998
• 页码: 269287
• 关键词: population-genetics; hypervariable loci; f-statistics; vntr loci; human dna; differentiation; identification; frequencies; caucasians; inference

摘要

As currently defined, DNA fingerprint profiles 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. With the help of Markov chain Monte Carlo sampling, levels of heterogeneity are estimated for three major ethnic groups in the database of Lifecodes Corporation.