@article{
 title = {Modelling crime linkage with Bayesian networks},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Bayesian networks,Case linkage,Combining evidence,Crime linkage,Serial crime},
 websites = {http://www.sciencedirect.com/science/article/pii/S1355030614001658},
 month = {12},
 id = {ac7a3036-3422-3b57-99f4-ced580bcaf8b},
 created = {2015-04-11T22:23:04.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {dd0b66b1-f60b-324e-aaa2-d1b8e5ba9e0c},
 last_modified = {2017-03-14T14:27:30.933Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {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.},
 bibtype = {article},
 author = {de Zoete, Jacob and Sjerps, Marjan and Lagnado, David and Fenton, Norman},
 journal = {Science & Justice}
}

{"_id":"s7h9b9aPK6zmpKFY4","authorIDs":[],"author_short":["de Zoete, J.","Sjerps, M.","Lagnado, D.","Fenton, N."],"bibbaseid":"dezoete-sjerps-lagnado-fenton-modellingcrimelinkagewithbayesiannetworks-2014","bibdata":{"title":"Modelling crime linkage with Bayesian networks","type":"article","year":"2014","identifiers":"[object Object]","keywords":"Bayesian networks,Case linkage,Combining evidence,Crime linkage,Serial crime","websites":"http://www.sciencedirect.com/science/article/pii/S1355030614001658","month":"12","id":"ac7a3036-3422-3b57-99f4-ced580bcaf8b","created":"2015-04-11T22:23:04.000Z","accessed":"2015-04-11","file_attached":false,"profile_id":"95e10851-cdf3-31de-9f82-1ab629e601b0","group_id":"dd0b66b1-f60b-324e-aaa2-d1b8e5ba9e0c","last_modified":"2017-03-14T14:27:30.933Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"private_publication":false,"abstract":"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.","bibtype":"article","author":"de Zoete, Jacob and Sjerps, Marjan and Lagnado, David and Fenton, Norman","journal":"Science & Justice","bibtex":"@article{\n title = {Modelling crime linkage with Bayesian networks},\n type = {article},\n year = {2014},\n identifiers = {[object Object]},\n keywords = {Bayesian networks,Case linkage,Combining evidence,Crime linkage,Serial crime},\n websites = {http://www.sciencedirect.com/science/article/pii/S1355030614001658},\n month = {12},\n id = {ac7a3036-3422-3b57-99f4-ced580bcaf8b},\n created = {2015-04-11T22:23:04.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {dd0b66b1-f60b-324e-aaa2-d1b8e5ba9e0c},\n last_modified = {2017-03-14T14:27:30.933Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {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.},\n bibtype = {article},\n author = {de Zoete, Jacob and Sjerps, Marjan and Lagnado, David and Fenton, Norman},\n journal = {Science & Justice}\n}","author_short":["de Zoete, J.","Sjerps, M.","Lagnado, D.","Fenton, N."],"urls":{"Website":"http://www.sciencedirect.com/science/article/pii/S1355030614001658"},"bibbaseid":"dezoete-sjerps-lagnado-fenton-modellingcrimelinkagewithbayesiannetworks-2014","role":"author","keyword":["Bayesian networks","Case linkage","Combining evidence","Crime linkage","Serial crime"],"downloads":0},"bibtype":"article","biburl":null,"creationDate":"2015-04-14T07:35:24.468Z","downloads":0,"keywords":["bayesian networks","case linkage","combining evidence","crime linkage","serial crime"],"search_terms":["modelling","crime","linkage","bayesian","networks","de zoete","sjerps","lagnado","fenton"],"title":"Modelling crime linkage with Bayesian networks","year":2014}