RFU derived LRs for activity level assignments using Bayesian Networks

Abstract

A comparative study has been carried out, comparing two different methods to estimate activity level likelihood ratios (𝐿𝑅 𝑎 ) using Bayesian Networks. The first method uses the sub-source likelihood ratio (𝑙𝑜𝑔 10 𝐿𝑅 𝜙 ) as a 'quality indicator'. However, this has been criticised as introducing potential bias from population differences in allelic proportions. An alternative method has been introduced that is based upon the total 𝑅𝐹 𝑈 of a DNA profile that is adjusted using the mixture proportion (𝑀 𝑥 ) which is calculated from quantitative probabilistic genotyping software (EuroForMix). Bayesian logistic regressions of direct transfer data showed that the two methods were comparable. Differences were attributed to sampling error, and small sample sizes of secondary transfer data. The Bayesian approach facilitates comparative studies by taking account of sampling error; it can easily be extended to compare different methods.

Who Packed the Drugs? Application of Bayesian Networks to Address Questions of DNA Transfer, Persistence, and Recovery from Plastic Bags and Tape

“…Previous work has usually applied discrete models to measure simple presence/absence of DNA that may be attributed to a person of interest (POI). A continuous model was demonstrated by Gill et al [ 6 ], based upon sub-source likelihood ratios, and this was recently complemented with a method using mean (peak height) instead of sub-source likelihood ratio [ 18 ]. Here we provide a demonstration of continuous modelling of indirect and direct transfer with two case examples.…”