Summary

Much research in recent years for evidence evaluation in forensic science has focused on methods for determining the likelihood ratio in various scenarios. When the issue in question is whether evidence is associated with a person who is or is not associated with criminal activity then the problem is one of discrimination. A procedure for the determination of the likelihood ratio is developed when the evidential data are believed to be driven by an underlying latent Markov chain. Three other models that assume auto-correlated data without the underlying Markov chain are also described. The performances of these four models and a model assuming independence are compared by using data concerning traces of cocaine on banknotes.

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
You do not currently have access to this article.