Simple line graph icon with four connected data points, depicted in dark blue on a light background.

Primary Author: Danica Ommen

Type: Webinar

This CSAFE webinar was held on August 25, 2022.

Presenter:
Danica Ommen
Assistant Professor – Department of Statistics, Iowa State University

Presentation Description:

To strengthen the statistical foundations of forensic evidence interpretation, likelihood ratios and Bayes factors are advocated to quantify the value of evidence. Both methods rely on formulating a statistical model, which can be challenging for complex evidence. Machine learning score-based likelihood ratios have been proposed as an alternative in those cases. Under this framework, a (dis)similarity score and its distribution under alternative propositions are estimated using pairwise comparisons, but pairwise comparisons of all the evidential objects result in dependent scores. While machine learning methods may not require distributional assumptions, most assume independence. We introduce a sampling and ensembling approach to remedy this lack of independence. We generate sets where assumptions are met to develop multiple score-based  likelihood ratios later aggregated into a final score to quantify the value of evidence.

Webinar Recording:

Read more about the study in this post


Related Resources

A thick gray wavy line forming an abstract, looping shape on a light gray background.

An Introduction to the Forensic Handwriting Analysis Software handwriter

July 18, 2025

Blue shoeprint with a tread pattern on a white background.

Forensic Footwear: A Retrospective of the Development of the MANTIS Shoe Scanning System

July 10, 2025

There currently are no shoe-scanning devices developed in the United States that can operate in a real-world, variable-weather environment in …

A green fingerprint icon on a light gray background.

Examiner consistency in perceptions of fingerprint minutia rarity

July 10, 2025

Friction ridge examiners (FREs) identify distinctive features (minutiae) in fingerprints and consider how rare these observed minutiae are in their …