Bayesian Analysis of DNA Profiling Data in Forensic Identification Applications
作者： Lindsey A. Foreman , Adrian F. M. Smith , Ian W. Evett
DOI: 10.1111/J.1467-985X.1997.00074.X
关键词: Profiling (information science) 、 Artificial intelligence 、 Microsatellite 、 Bayesian probability 、 Dna evidence 、 Forensic identification 、 DNA profiling 、 Computer science 、 Machine learning 、 Econometrics 、 Population 、 Population Heterogeneity 、 Statistics, Probability and Uncertainty 、 Economics and Econometrics 、 Statistics and Probability 、 Social Sciences (miscellaneous)
摘要: The utilization of DNA evidence in cases forensic identification has become widespread over the last few years. strength this against an individual standing trial is typically presented court form a likelihood ratio (LR) or its reciprocal (the profile match probability). value LR will vary according to nature genetic relationship between accused and other possible perpetrators crime population. This paper develops ideas methods for analysing data evaluating LRs when based on short tandem repeat profiles, with special emphasis placed Bayesian approach. These are then applied context particular quadruplex profiling system used routine case-work by UK Forensic Science Service.
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