Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Détails

ID Serval
serval:BIB_2E686EA07435
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis
Périodique
Journal of Forensic Science and Medicine
Auteur(s)
Sironi E., Taroni F.
ISSN
2349-5014
Statut éditorial
Publié
Date de publication
12/2015
Peer-reviewed
Oui
Volume
1
Numéro
2
Pages
124-132
Langue
anglais
Résumé
Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.
Mots-clé
Age estimation, Bayesian networks, forensic interpretation, forensic medicine, transition analysis
Open Access
Oui
Création de la notice
06/01/2016 7:44
Dernière modification de la notice
20/08/2019 13:12
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