Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt 
mixtures including a novel precision benchmark.

Susik, Mateusz; Sbalzarini, Ivo F.

doi:10.1016/j.fsigen.2023.102840

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark.

MPS-Authors

Susik, Mateusz
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/persons/resource/persons219620

Sbalzarini, Ivo F.
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

External Resource

https://publications.mpi-cbg.de/Susik_2023_8510.pdf
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Susik, M., & Sbalzarini, I. F. (2023). Analysis of the Hamiltonian Monte Carlo genotyping algorithm on PROVEDIt mixtures including a novel precision benchmark. Forensic science international. Genetics, 64: 102840. doi:10.1016/j.fsigen.2023.102840.

Cite as: https://hdl.handle.net/21.11116/0000-000E-AAA4-9

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).