Using sensitivity analyses in Bayesian Networks to highlight the impact of data paucity and direct future analyses: a contribution to the debate on measuring and reporting the precision of likelihood ratios

Détails

ID Serval
serval:BIB_594613013EC2
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Using sensitivity analyses in Bayesian Networks to highlight the impact of data paucity and direct future analyses: a contribution to the debate on measuring and reporting the precision of likelihood ratios
Périodique
Science & Justice
Auteur⸱e⸱s
Taylor Duncan, Hicks Tacha, Champod Christophe
ISSN
1355-0306
ISSN-L
1355-0306
Statut éditorial
Publié
Date de publication
09/2016
Peer-reviewed
Oui
Volume
56
Numéro
5
Pages
402-410
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
Bayesian networks are being increasingly used to address complex questions of forensic interest. Like all probabilities, those that underlie the nodes within a network rely on structured data and knowledge. Obviously, the more structured data we have, the better. But, in real life, the numbers of experiments that can be carried out are limited. It is thus important to know if/when our knowledge is sufficient and when one needs to perform further experiments to be in a position to report the value of the observations made. To explore the impact of the amount of data that are available for assessing results, we have constructed Bayesian Networks and explored the sensitivity of the likelihood ratios to changes to the data that underlie each node. Bayesian networks are constructed and sensitivity analyses performed using freely available R libraries (gRain and BNlearn). We demonstrate how the analyses can be used to yield information about the robustness provided by the data used to inform the conditional probability table, and also how they can be used to direct further research for maximum effect. By maximum effect, we mean to contribute with the least investment to an increased robustness. In addition, the paper investigates the consequences of the sensitivity analysis to the discussion on how the evidence shall be reported for a given state of knowledge in terms of underpinning data.

Mots-clé
Sensitivity analysis, Bayesian networks, Likelihood ratio, Data, Source level propositions, Pathology and Forensic Medicine
Pubmed
Web of science
Création de la notice
23/11/2017 6:32
Dernière modification de la notice
20/08/2019 14:12
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