Modelling crime linkage with Bayesian networks

Autor:	David A. Lagnado, Norman Fenton, Marjan Sjerps, Jacob de Zoete
Přispěvatelé:	Stochastics (KDV, FNWI)
Jazyk:	angličtina
Rok vydání:	2015
Předmět:	Bayesian network Poison control Linkage (mechanical) Exculpatory evidence Serial crime Computer security computer.software_genre Unlink Pathology and Forensic Medicine law.invention law Econometrics Degree of similarity Optimal distinctiveness theory Psychology computer
Zdroj:	Science & justice, 55(3), 209-217. Forensic Science Society
ISSN:	1355-0306
Popis:	When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model different evidential structures that can occur when linking crimes, and how they assist in understanding the complex underlying dependencies. That is, how evidence that is obtained in one case can be used in another and vice versa. The flip side of this is that the intuitive decision to "unlink" a case in which exculpatory evidence is obtained leads to serious overestimation of the strength of the remaining cases.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f323a641d76e2f623c94ef738a543dd https://hdl.handle.net/11245/1.507566 Zobrazit plný text záznamu Full Text from ScienceDirect