Estimation of the bayes' factor in a forensic science problem

Abstract

The Bayes' factor, or likelihood ratio, plays an important role in the assessment of forensic evidence. Four methods of determining the Bayes' factor are developed. Backgraound data collected by forensic scientists often have a random effects structure where the random effects do not have a Normal distribution. The methods of Assessing these data compare results obtained where a group structure in the back ground data is and is not assumed, and where the within group variance is and is not assumed known. The distribution of the random effects is modelled using kernel density estimation. A simulation study shown that an improvement over the Normality assumption of the Bayes' factor estimates is obtained by using a kernel method when the random effects are not Normally distributed.