COMBINING UNCERTAINTIES IN A COURT OF LAW USING BAYESIAN NETWORKS

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People generally have difficulty dealing with the counter-intuitive notion of probability, and therefore they often misunderstand aspects of uncertainty. This is particularly significant in a court of law when for example an estimate of the probability of the evidence gets confused with an estimate of the probability of guilt. The circumstantial evidence is especially prone to being handled incorrectly. Professor Fenton at the Queen Mary University of London said, “You could argue that virtually every case with circumstantial evidence is ripe for being improved by Bayesian arguments”. In this paper, the evidence in a famous court case is revisited in the context of Bayesian networks.

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