English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Forensic scientists are often criticised for the lack of quantitative supportfor the conclusions of their examinations. While scholars advocate for the useof a Bayes factor to quantify the weight of forensic evidence, it is oftenimpossible to assign the necessary probability measures to performlikelihood-based inference for high-dimensional and complex data. To addressthis issue, we revisit a two-stage inference framework and leverage theproperties of kernel functions to offer a method that allows for statisticallysupporting the inference of the identity of source of sets of trace and controlobjects by way of a single test. Our method is generic in that it can be easilytailored to any type of data encountered in forensic science or patternrecognition, and our method does not depend on the dimension or the type of theconsidered data. The application of our method to paint evidence shows thatthis type of evidence carries substantial probative value. Finally, ourapproach can easily be extended to other evidence types such as glass, fibresand dust.

Two-stage approach for the inference of the source of high-dimension and complex chemical data in forensic science