Bayesian Networks and Probabilistic Inference in Forensic Science

The amount of information forensic scientists are able to offer is ever increasing, owing to vast developments in science and technology. Consequently, the complexity of evidence does not allow scientists to cope adequately with the problems it causes, or to make the required inferences. Probability theory, implemented through graphical methods, specifically Bayesian networks, offers a powerful tool to deal with this complexity, and discover valid patterns in data. The book provides a unique and comprehensive introduction to the use of Bayesian networks for the evaluation of scientific evidence in forensic science. It includes self-contained introduction to both Bayesian networks and probability; features implementation of the methodology using HUGIN, the leading Bayesian networks software, presents basic standard networks that can be implemented in available software packages, and that form the core models necessary for the reader’s own analysis of real cases; provides a technique for structuring problems and organizing uncertain data based on methods and principles of scientific reasoning; contains a method for constructing coherent and defensible arguments for the analysis and evaluation of forensic evidence.