ALBERT

Description: 〈p〉Publication date: October 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Heliyon, Volume 4, Issue 10〈/p〉 〈p〉Author(s): Mark W. Perlin〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Natural variation in biological evidence leads to uncertain genotypes. Forensic comparison of a probabilistic genotype with a person's reference gives a numerical strength of DNA association. The distribution of match strength for all possible references usefully represents a genotype's potential information. But testing more genetic loci exponentially increases the number of multi-locus possibilities, making direct computation infeasible.〈/p〉 〈p〉At each locus, Bayesian probability can quickly assemble a match strength random variable. Multi-locus match strength is the sum of these independent variables. A multi-locus genotype's match strength distribution is efficiently constructed by convolving together the separate locus distributions. This convolution construction can accurately collate all trillion trillion reference outcomes in a fraction of a second.〈/p〉 〈p〉This paper shows how to rapidly construct multi-locus match strength distributions by convolution. Function convergence demonstrates that distribution accuracy increases with numerical resolution. Convolution construction has quadratic computational complexity, relative to the exponential number of reference genotypes. A suitably defined random variable reduces high-dimensional computational cost to fast real-line arithmetic.〈/p〉 〈p〉Match strength distributions are used in forensic validation studies. They provide error rates for match results. The convolution construction applies to discrete or continuous variables in the forensic, natural and social sciences. Computer-derived match strength distributions elicit the information inherent in DNA evidence, often overlooked by human analysis.〈/p〉 〈/div〉 〈/div〉