This project developed a two-stage approach for the inference of the source of high-dimension and complex chemical data in forensic science.
Forensic scientists are often criticized for the lack of quantitative support for the conclusions of their examinations. Although scholars advocate for the use of a Bayes factor to quantify the weight of forensic evidence, it is often impossible to assign the necessary probability measures to perform likelihood-based inference for high-dimensional and complex data. To address this issue, The current project revisited a two-stage inference framework and leveraged the properties of kernel functions to offer a method that allows for statistically supporting the inference of the identity of source of sets of trace and control objects by way of a single test. This method is generic in that it can be easily tailored to any type of data encountered in forensic science or pattern recognition, and it does not depend on the dimension or the type of the considered data. The application of our method to paint evidence shows that this type of evidence carries substantial probative value. Finally, this approach can easily be extended to other evidence types such as glass, fibers, and dust. (publisher abstract modified)
Report (Grant Sponsored)
Date Published: January 1, 2019