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A Bayesian nonparametric approach for the rare type problem

Presented by: 
Giulia Cereda Université de Lausanne, Universiteit Leiden
Date: 
Friday 11th November 2016 - 09:45 to 10:30
Venue: 
INI Seminar Room 1
Abstract: 

 

Co-author: Richard Gill (Leiden University)
The evaluation of a match between the DNA profile of a stain found on a crime scene and that of a suspect (previously identified) involves the use of the unknown parameter p=(p1, p2, ...), (the ordered vector which represents the proportions of the different DNA profiles in the population of potential donors) and the names of the different DNA types.

We propose a Bayesian nonparametric method which considers p as the realization of a random variable P distributed according to the two-parameter Poisson Dirichlet and discard information about DNA types.

The ultimate goal of this model is to evaluate DNA matches in the rare type case, that is the situation in which the suspect's profile, matching the crime stain profile, is not one of those in the database of reference. This situation is so problematic that has been called “the fundamental problem of forensic mathematics” by Charles Brenner. 

 

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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons