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10 octobre 2019
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.fsigen.2019.102172
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info:eu-repo/semantics/altIdentifier/pmid/31629186
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info:eu-repo/semantics/altIdentifier/pissn/1872-4973
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info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_E4C5F2B13A1F2
info:eu-repo/semantics/openAccess , CC BY-NC-ND 4.0 , https://creativecommons.org/licenses/by-nc-nd/4.0/
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Ilaria De March et al., « Bayesian networks and dissonant items of evidence: A case study », Serveur académique Lausannois, ID : 10.1016/j.fsigen.2019.102172
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Résumé
The assessment of different items of evidence is a challenging process in forensic science, particularly when the relevant elements support different inferential directions. In this study, a model is developed to assess the joint probative value of three different analyses related to some biological material retrieved on an object of interest in a criminal case. The study shows the ability of probabilistic graphical models, say Bayesian networks, to deal with complex situations, those that one expects to face in real cases. The results obtained by the model show the importance of a conflict measure as an indication of inconsistencies in the model itself. A contamination event alleged by the defense is also introduced in the model to explain and solve the conflict. The study aims to give an insight in the application of a probabilistic model to real criminal cases.