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  1. Norman Fenton, Martin Neil & Anne Hsu (forthcoming). Calculating and Understanding the Value of Any Type of Match Evidence When There Are Potential Testing Errors. Artificial Intelligence and Law:1-28.
    It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases (...)
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  2. Norman Fenton, Martin Neil & David A. Lagnado (2013). A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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  3. David A. Lagnado, Norman Fenton & Martin Neil (2013). Legal Idioms: A Framework for Evidential Reasoning. Argument and Computation 4 (1):46 - 63.
    (2013). Legal idioms: a framework for evidential reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 46-63. doi: 10.1080/19462166.2012.682656.
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  4. Norman Fenton, Paul Krause & Martin Neil (2002). Probabilistic Modelling for Software Quality Control. Journal of Applied Non-Classical Logics 12 (2):173-188.
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