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Forensic Data Science |
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Calculation of Likelihood Ratios |
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Forensic Data Science |
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Calculation of Likelihood Ratios |
Calculation of likelihood ratios is also covered on other webpages within this website.
This page relates to reseach that does not fit into the topics of the other pages.
Selected Publications
- Morrison G.S. (2025). Taking account of typicality in calculation of likelihood ratios. Law, Probabaility & Risk, 24, mgaf009.
https://doi.org/10.1093/lpr/mgaf009
- Morrison G.S., Poh N. (2018). Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios / Bayes factors. Science & Justice, 58, 200–218.
https://doi.org/10.1016/j.scijus.2017.12.005
- Morrison G.S., Enzinger E. (2018). Score based procedures for the calculation of forensic likelihood ratios – Scores should take account of both similarity and typicality. Science & Justice, 58, 47–58.
https://doi.org/10.1016/j.scijus.2017.06.005
Project:
Taking account of typicality in calculation of likelihood ratios
Manuscript:
- Morrison G.S. (2024). Taking account of typicality in calculation of likelihood ratios. Law, Probabaility & Risk, 24, mgaf009. https://doi.org/10.1093/lpr/mgaf009
When calculating a likelihood ratio with respect to the question of whether two items originated from the same source or from different sources, one must take account of not only the similarity between the items but also their typicality with respect to the relevant population. Using simple univariate examples, this paper demonstrates that likelihood ratios calculated using specific-source and common-source methods do take account of typicality, but that likelihood ratios calculated from similarity scores do not. It also demonstrates that converting feature values to percentile-rank values before calculating similarity scores does not properly take account of typicality. The paper argues that methods that do not take account of typicality should not be used, and that methods that do take account of typicality should be used instead. Since sufficient case-relevant data to train a specific-known-source model are seldom available, the paper recommends that the method to use instead of the similarity-score method should usually be the common-source method.
Software and data:
- Matlab code that runs the synthetic-data-based demonstrations and experiments described in the manuscript.
https://forensic-data-science.net/likelihood-ratio-calculation/
This webpage is maintained by Geoffrey Stewart Morrison and was last updated 2025-08-11