Using already-solved cases of a mass disaster event for prioritizing the search among remaining victims: a Bayesian approach.

This work presents a new method for assisting in the identification process of missing persons in several contexts, such as enforced disappearances. We apply a Bayesian technique to incorporate non-genetic variables in the construction of prior information. In that way, we can learn from the already-solved cases of a particular mass event of death, and use that information to guide the search among remaining victims. This paper describes a particular application to the proposed method to the identification of human remains of the so-called disappeared during the last dictatorship in Argentina, which lasted from 1976 until 1983. Potential applications of the techniques presented hereby, however, are much wider. The central idea of our work is to take advantage of the already-solved cases within a certain event to use the gathered knowledge to assist in the investigation process, enabling the construction of prioritized rankings of victims that could correspond to each certain unidentified human remains. [ABSTRACT FROM AUTHOR]