Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Détails

ID Serval
serval:BIB_D8F4846080D4
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"
Périodique
Science and Justice
Auteur⸱e⸱s
Biedermann A., Voisard R., Taroni F.
ISSN
1355-0306
ISSN-L
1355-0306
Statut éditorial
Publié
Date de publication
09/2012
Peer-reviewed
Oui
Volume
52
Numéro
3
Pages
191-198
Langue
anglais
Résumé
Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.
Mots-clé
Bayesian networks, Likelihood ratio, Multiple propositions, Handwriting examination, Teaching
Création de la notice
27/08/2012 7:13
Dernière modification de la notice
20/08/2019 15:58
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