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Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science, 2nd Edition by Colin Aitken, Paolo Garbolino, Silvia Bozza, Alex Biedermann, Franco Taroni

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Preface to the first edition

Forensic scientists may have hardly ever been able to gather and offer as much information, analytical or otherwise, as is possible today. Owing to advances made in science and technology, today's forensic scientists can choose amongst a broad scope of methods and techniques, applicable to various kinds of evidence even in their remotest quantities. However, despite the potentially abundant amount of available information, there now is an increased awareness amongst a significant part of the forensic community – including legal scholars – that there are risks associated with these tendencies. A sense of some sort of overconfidence, for instance.

Scientific evidence as encountered in the real world is always incomplete to some degree, thus uncertainty is a prevalent element with which forensic scientists have to deal. Evidence does not tell anything as for itself; its significance needs to be elucidated in the light of competing propositions and given background knowledge about the case at hand. There is a great practical necessity for forensic scientists of advising their clients, be they lawyers, prosecutors, jurors or decision makers at large, of the significance of their findings. Forensic scientists are required to qualify and, where possible, quantify their states of knowledge and to be consultants in assessing uncertainties associated with the inferences that may be drawn from forensic evidence.

For this task, forensic scientists should consider ...

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