title: Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature.
abstract: Almost 30 years ago, Bayesian networks (BNs) were 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. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research 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. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.
authors.affiliation: University of Lausanne, School of Criminal Justice, Institute of Forensic Science, le batochime, 1015 Lausanne-Dorigny, Switzerland. alex.biedermann@unil.ch
authors.forename: A
authors.initials: A
authors.lastname: Biedermann
authors.forename: F
authors.initials: F
authors.lastname: Taroni
mesh_terms: D001499:Bayes Theorem; D016172:DNA Fingerprinting; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010334:Paternity
chemical_list: D005819:Genetic Markers