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Verfasst von:Stahlschmidt, Stephan [VerfasserIn]   i
 Tausendteufel, Helmut [VerfasserIn]   i
 Härdle, Wolfgang [VerfasserIn]   i
Titel:Bayesian networks for sex-related homicides
Titelzusatz:structure learning and prediction
Verf.angabe:Stephan Stahlschmidt, Helmut Tausendteufel, Wolfgang K. Härdle
Jahr:2013
Umfang:17 S.
Fussnoten:Published online: 19 Mar 2013 ; Gesehen am 15.02.2022
Titel Quelle:Enthalten in: Journal of applied statistics
Ort Quelle:Abingdon [u.a.] : Taylor & Francis, Taylor & Francis Group, 1974
Jahr Quelle:2013
Band/Heft Quelle:40(2013), 6, Seite 1155-1171
ISSN Quelle:1360-0532
Abstract:Sex-related homicides tend to arouse wide media coverage and thus raise the urgency to find the responsible offender. However, due to the low frequency of such crimes, domain knowledge lacks completeness. We have therefore accumulated a large data-set and apply several structural learning algorithms to the data in order to combine their results into a single general graphic model. The graphical model broadly presents a distinction between an offender and a situation-driven crime. A situation-driven crime may be characterised by, amongst others, an offender lacking preparation and typically attacking a known victim in familiar surroundings. On the other hand, offender-driven crimes may be identified by the high level of forensic awareness demonstrated by the offender and the sophisticated measures applied to control the victim. The prediction performance of the graphical model is evaluated via a model averaging approach on the outcome variable offender's age. The combined graph undercuts the error rate of the single algorithms and an appropriate threshold results in an error rate of less than 10%, which describes a promising level for an actual implementation by the police.
DOI:doi:10.1080/02664763.2013.780235
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag ; Resolving-System: https://doi.org/10.1080/02664763.2013.780235
 Volltext: https://www.tandfonline.com/doi/full/10.1080/02664763.2013.780235?scroll=top&needAccess=true
 DOI: https://doi.org/10.1080/02664763.2013.780235
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:62-09
 62P25
 Bayesian networks
 C49
 C81
 criminal event perspective
 ensemble learning
 K42
 model averaging
 offender profiling
K10plus-PPN:1789584159
Verknüpfungen:→ Zeitschrift

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