Abstract
AbstractWe provide an internal validation study of a recently published precise DNA mixture algorithm based on Hamiltonian Monte Carlo sampling [1]. We provide results for all 428 mixtures analysed by Riman et al. [2] 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. [3].
Publisher
Cold Spring Harbor Laboratory
Reference27 articles.
1. Hamiltonian monte carlo with strict convergence criteria reduces run-to-run variability in forensic DNA mixture deconvolution;Forensic Science International: Genetics,2022
2. Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset;PLOS ONE,2021
3. John Buckleton , Jo-Anne Bright , Duncan Taylor , Richard Wivell , Øyvind Bleka , Peter Gill , Corina Benschop , Bruce Budowle , and Michael Coble . Re: Riman et al. examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset. Forensic Science International: Genetics, page 102709, April 2022.
4. John S. Buckleton , Jo-Anne Bright , and Duncan Taylor , editors. Forensic DNA Evidence Interpretation. CRC Press, paperback edition, 3 2021.
5. Kevin Cheng , Øyvind Bleka , Peter Gill , James Curran , Jo-Anne Bright , Duncan Taylor , and John Buckleton . A comparison of likelihood ratios obtained from EuroForMix and STRmix™. Journal of Forensic Sciences, September 2021.
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