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  1. A General Structure for Legal Arguments About Evidence Using Bayesian Networks.Norman Fenton, Martin Neil & David A. Lagnado - 2013 - 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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  2.  41
    Modelling Competing Legal Arguments Using Bayesian Model Comparison and Averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...)
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  3.  24
    Legal Idioms: A Framework for Evidential Reasoning.David A. Lagnado, Norman Fenton & Martin Neil - 2013 - 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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    Learning From Behavioural Changes That Fail.Magda Osman, Scott McLachlan, Norman Fenton, Martin Neil, Ragnar Löfstedt & Björn Meder - 2020 - Trends in Cognitive Sciences 24 (12):969-980.
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    Analyzing the Simonshaven Case Using Bayesian Networks.Norman Fenton, Martin Neil, Barbaros Yet & David Lagnado - 2020 - Topics in Cognitive Science 12 (4):1092-1114.
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  6.  30
    Calculating and Understanding the Value of Any Type of Match Evidence When There Are Potential Testing Errors.Norman Fenton, Martin Neil & Anne Hsu - 2014 - Artificial Intelligence and Law 22 (1):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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    Probabilistic Modelling for Software Quality Control.Norman Fenton, Paul Krause & Martin Neil - 2002 - Journal of Applied Non-Classical Logics 12 (2):173-188.
    As is clear to any user of software, quality control of software has not reached the same levels of sophistication as it has with traditional manufacturing. In this paper we argue that this is because insufficient thought is being given to the methods of reasoning under uncertainty that are appropriate to this domain. We then describe how we have built a large-scale Bayesian network to overcome the difficulties that have so far been met in software quality control. This exploits a (...)
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