• Approximations;
  • Decision analysis;
  • DNA short tandem repeat markers;
  • High dimensional spaces;
  • Kinship identification;
  • Likelihood ratio distribution


We show how to evaluate a kinship identification system, which is a probabilistic tool devoted to obtain the likelihood ratio supporting the hypothesis that an individual, the candidate for identification, is a specific member of a family, conditional on the available familial DNA evidence. The paper considers the likelihood ratio as a random variable and focuses on the evaluation of the probability that a candidate for identification would be correctly classified, exploiting the likelihood ratio distributions conditional on each hypothesis. The system evaluation proposed is specific for each case, does not require any additional laboratory costs and should be carried out before the identification trial is performed. In a pre-experimental perspective, we evaluate whether a system fulfils the requirements of the parties that are involved. Special attention is devoted to the computational aspects of the problem: since high dimensional spaces are involved, matters concerning approximations are discussed. Some real cases using the methodology proposed are presented and discussed.