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Titolo: Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey.
Autore: Michael Riesen; Gursel Serpen;

 Titolo Testata:
 Artificial intelligence and law
fascicolo: 3,
volume: 16,
anno: 2008,
pagine: 245  276
 SICI:
 09248463(2008)16:3<245:>2.0.ZU;2T
 Fonte:
 UNIBO
 Lingua:
 ENG
 Keywords:
 National crime victimization survey; Bayesian belief network; machine learning; probalistic query; posterior probability calculations; joint probability distribution; model validation;
 Tipo documento:
 Article
 Natura:
 Periodico

 Recensione:
 Indirizzi per estratti:



 Citazione:
 Michael Riesen e Gursel Serpen, "Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey.", Artificial intelligence and law, 16(3), 2008, pp. 245276
Abstract
This paper presents an effort to induce a Bayesian belief network (BBN) from crime data,namely the national crime victimization survey (NCVS).This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidens,with particular focus on characteristics of the victim.The goals are to generate a BBN to capture how characteristics of crime incidens are related to one another,and to make this information available to crime incidents are related to one anothe,and to make this information available to domain specialists.The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to nonexperts in a way that facilitates automated analysis.Validation of the BBN's ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished througth a variety of sources including mathematical techniques and human experts for appropriate triangulation.Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain,and accordingly can serve as an effective query tool.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 10/04/20 alle ore 02:13:21