Bayesian Networks and Probabilistic Inference in Forensic Science

Abstract

From the Wiley website: `Probability theory, implemented through graphical methods, specifically Bayesian networks, offers a powerful tool to deal with complex questions in forensic science and discover valid patterns in data. [This book] provides a unique and comprehensive introduction to the use of Bayesian networks for the evaluation of scientific evidence in forensic science.' The book opens with several introductory chapters on probability, Bayesian networks (BNs) and basic principles of evidence evaluation. There follow chapters on DNA evidence and transfer evidence such as fibres. The final chapters cover some more advanced general topics such as combinations of evidence, sensitivity analysis and qualitative and continuous networks. The level of discussion is reasonably elementary and at a leisurely pace, allowing an interested reader with little mathematical training to follow the arguments. But do not let me mislead you into thinking this is `light reading', the issues in forensic problems can be subtle and there are many aspects to consider, so one can easily get lost in complexities. The theoretical development is illustrated with simple examples worked through in considerable detail. Indeed, while the level of detail provided for the early examples will be appreciated by many readers, as the book advances the examples are still treated in great detail, which I feel precluded more substantial and realistic examples. For the very simple problems amenable to such treatment, the overhead in setting up a problem in the BN formalism can seem not worth the benefit. In practice, the reader is encouraged to use BN software to explore further examples on their own; specifically, the authors use the HUGIN package for which a `Lite' version is available free.