We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling (Susik et al., 2022). We provide results for all 428 mixtures analysed by Riman et al. (2021) and compare the results with two state-of-the-art software products: STRmix� v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. (2022).
%0 Journal Article
%1 SUSIK2023102840
%A Susik, Mateusz
%A Sbalzarini, Ivo F.
%D 2023
%J Forensic Science International: Genetics
%K DNA HMC Probabilistic Validation, genotyping, mixtures,
%P 102840
%R https://doi.org/10.1016/j.fsigen.2023.102840
%T Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark
%U https://www.sciencedirect.com/science/article/pii/S1872497323000157
%V 64
%X We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling (Susik et al., 2022). We provide results for all 428 mixtures analysed by Riman et al. (2021) and compare the results with two state-of-the-art software products: STRmix� v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. (2022).
@article{SUSIK2023102840,
abstract = {We provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling (Susik et al., 2022). We provide results for all 428 mixtures analysed by Riman et al. (2021) and compare the results with two state-of-the-art software products: STRmix� v2.6 and Euroformix v3.4.0. The comparison shows that the Hamiltonian Monte Carlo method provides reliable values of likelihood ratios (LRs) close to the other methods. We further propose a novel large-scale precision benchmark and quantify the precision of the Hamiltonian Monte Carlo method, indicating its improvements over existing solutions. Finally, we analyse the influence of the factors discussed by Buckleton et al. (2022).},
added-at = {2024-10-15T13:24:46.000+0200},
author = {Susik, Mateusz and Sbalzarini, Ivo F.},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/218149edd10ee9e68c7dd4584d6682fb9/scadsfct},
doi = {https://doi.org/10.1016/j.fsigen.2023.102840},
interhash = {eb464e4e32be67aa8d978c106a025ef4},
intrahash = {18149edd10ee9e68c7dd4584d6682fb9},
issn = {1872-4973},
journal = {Forensic Science International: Genetics},
keywords = {DNA HMC Probabilistic Validation, genotyping, mixtures,},
pages = 102840,
timestamp = {2024-10-15T13:24:46.000+0200},
title = {Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark},
url = {https://www.sciencedirect.com/science/article/pii/S1872497323000157},
volume = 64,
year = 2023
}
