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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 second edition

Suppose that you are a forensic scientist, facing a large quantity of information coming from various observations, data or, more generally, findings related to a case under investigation. Your task is to help express a probabilistic conclusion on the joint value of such a quantity items of information or to assist a court of justice in expressing a belief on a judicial question of interest, typically expressed in terms of a proposition, compared to a particular alternative. How should you proceed? Ten years ago, Professor Dennis Lindley wrote in his foreword for another book of two of us (Aitken and Taroni 2004, p. 24):

A problem that arises in a courtroom, affecting both lawyers, witnesses and jurors, is that several pieces of evidence have to be put together before a reasoned judgement can be reached: as when motive has to be considered along with material evidence. Probability is designed to effect such combinations but the accumulation of simple rules can produce complicated procedures. Methods of handling sets of evidence have been developed: for example Bayes nets (...). There is a fascinating interplay here between the lawyer and the scientist where they can learn from each other and develop tools that significantly assist in the production of a better judicial system.

Indeed, during the past three decades, the so-called Bayesian networks have gradually become a centre of attention for researchers from several academic fields. Whenever ...

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