Bayesian Networks

Abstract

Bayesian networks are graphical models developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Bayesian networks are also of interest to researchers in forensic science, and this tendency has considerably intensified throughout the last decade. There is now a considerable body of scientific literature that reports on Bayesian networks as a tool that can be used to study, develop, and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Within this context, evaluative issues that pertain to forensic DNA profiling evidence hold an important position because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. This article presents a sketch of the concept of Bayesian networks and its use as a complementary contribution to the body of analytical techniques that are needed to approach forensic inference problems in accordance with probability theory, both conceptually and practically.