Evaluation of the evidential value of physicochemical data by a Bayesian network approach

The growing interest in applications of Bayesian networks (BNs) in forensic science raises the question of whether BN could be used in forensic practice for the evaluation of results from physicochemical analysis of a limited number of observations from flammable liquids (weathered kerosene and diesel fuel) by automated thermal desorption gas chromatography mass spectrometry (ATD‐GC/MS), car paints by pyrolysis gas chromatography mass spectrometry (Py‐GC/MS) and fibres by microspectrophotometry (MSP) in the visible (VIS) range. Therefore, various simple BN models, which allow the evaluation of both discrete and continuous types of data, were studied in order to address questions raised by the representatives of the administration of justice, concerning the identification and classification of objects into certain categories and/or the association between two items. The results of the evaluation performed by BN models were expressed in the form of a likelihood ratio, which is a well‐documented measure of evidential value in the forensic field. From the results obtained, it can be concluded that BN models seem to be promising tool for evaluating physicochemical data. Copyright © 2010 John Wiley & Sons, Ltd.

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