A Bayesian Approach to Interpreting Latent Fingerprint Evidence
Wednesday, December 6, 2017 - 3:30pm
In 2009, the National Research Council issued Strengthening Forensic Science in the United States: A Path Forward, a report about the need for more scientific rigor in forensic science. Since then, there has been an effort to increase objectivity in forensic methods, in part through the use of statistics to interpret forensic evidence. In this talk, we demonstrate ways in which Bayesian inference can be used to interpret latent fingerprint evidence. We explore the dependencies in evidence that exist when multiple suspects are examined as the potential source of a latent print and propose a way to account for these dependencies by imposing a constraint on the model set up. Then, we discuss how a well-resourced database can be used to understand the variability between multiple prints produced by the same finger and provide recommendations about what characteristics such a database may have.
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