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A Nonparametric Bayesian Approach to the Rare Type Match Problem

Mathematical Institute, Leiden University, Postbus 9512, 2300 RA Leiden, The Netherlands
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Entropy 2020, 22(4), 439; https://doi.org/10.3390/e22040439
Received: 27 January 2020 / Revised: 2 April 2020 / Accepted: 9 April 2020 / Published: 13 April 2020
The “rare type match problem” is the situation in which, in a criminal case, the suspect’s DNA profile, matching the DNA profile of the crime stain, is not in the database of reference. Ideally, the evaluation of this observed match in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person) should be based on the calculation of the likelihood ratio and depends on the population proportions of the DNA profiles that are unknown. We propose a Bayesian nonparametric method that uses a two-parameter Poisson Dirichlet distribution as a prior over the ranked population proportions and discards the information about the names of the different DNA profiles. This model is validated using data coming from European Y-STR DNA profiles, and the calculation of the likelihood ratio becomes quite simple thanks to an Empirical Bayes approach for which we provided a motivation. View Full-Text
Keywords: forensic statistics; likelihood ratio; Bayesian nonparametric; rare type match problem; Y-STR forensic statistics; likelihood ratio; Bayesian nonparametric; rare type match problem; Y-STR
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Cereda, G.; Gill, R.D. A Nonparametric Bayesian Approach to the Rare Type Match Problem. Entropy 2020, 22, 439.

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