Skip to content

Machine Learning Methods for Dependent Data Resulting from Forensic Evidence Comparisons

Conference/Workshop:
Joint Statistical Meetings (JSM)
Published: 2021
Primary Author: Danica Ommen
Secondary Authors: Federico Veneri
Research Area: Forensic Statistics

Presented at the Joint Statistical Meeting in 2021

Related Resources

The q–q Boxplot

The q–q Boxplot

Boxplots have become an extremely popular display of distribution summaries for collections of data, especially when we need to visualize summaries for several collections simultaneously. The whiskers in the boxplot…
The Contribution of Forensic and Expert Evidence to DNA Exoneration Cases: An Interim Report

The Contribution of Forensic and Expert Evidence to DNA Exoneration Cases: An Interim Report

This report is from Simon A. Cole, Vanessa Meterko, Sarah Chu, Glinda Cooper, Jessica Weinstock Paredes, Maurice Possley, and Ken Otterbourg (2022), The Contribution of Forensic and Expert Evidence to…
Likelihood ratios for categorical count data with applications in digital forensics

Likelihood ratios for categorical count data with applications in digital forensics

We consider the forensic context in which the goal is to assess whether two sets of observed data came from the same source or from different sources. In particular, we…
CSAFE Project Update & ASCLD FRC Collaboration

CSAFE Project Update & ASCLD FRC Collaboration

This presentation highlighted CSAFE’s collaboration with the ASCLD FRC Collaboration Hub.