Estimation of Parameters in DNA Mixture Analysis

TL;DR

This paper introduces maximum likelihood and Bayesian methods for estimating variance and contributor proportions in DNA mixture analysis, improving accuracy and incorporating prior knowledge for forensic applications.

Contribution

It presents novel maximum likelihood estimation techniques for variance and contributor proportions, and integrates Bayesian analysis into DNA mixture interpretation.

Findings

Maximum likelihood estimates improve parameter accuracy.

Bayesian methods incorporate prior knowledge effectively.

Application to casework demonstrates practical utility.

Abstract

In Cowell et al. (2007), a Bayesian network for analysis of mixed traces of DNA was presented using gamma distributions for modelling peak sizes in the electropherogram. It was demonstrated that the analysis was sensitive to the choice of a variance factor and hence this should be adapted to any new trace analysed. In the present paper we discuss how the variance parameter can be estimated by maximum likelihood to achieve this. The unknown proportions of DNA from each contributor can similarly be estimated by maximum likelihood jointly with the variance parameter. Furthermore we discuss how to incorporate prior knowledge about the parameters in a Bayesian analysis. The proposed estimation methods are illustrated through a few examples of applications for calculating evidential value in casework and for mixture deconvolution.