This CSAFE webinar was held on August 25, 2022.<\/p>\n
Presenter:<\/strong> Presentation Description:<\/strong><\/p>\n 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.\u00a0Machine learning score-based\u00a0likelihood 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.\u00a0We introduce a sampling and ensembling approach to remedy this lack of independence. We generate sets where assumptions are met to develop multiple score-based\u00a0\u00a0likelihood ratios later aggregated into a final score to quantify the value of evidence.<\/p>\n Webinar Recording:<\/strong><\/p>\n
\nDanica Ommen
\nAssistant Professor \u2013 Department of Statistics, Iowa State University<\/p>\n