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NCJRS Abstract

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  NCJ Number: NCJ 242041     Find in a Library
  Title: Bayesian Networks and the Value of the Evidence for the Forensic Two-Trace Transfer Problem
  Document URL: HTML 
  Author(s): Simone Gittelson, M.Sc. ; Alex Biedermann, Ph.D. ; Silvia Bozza, Ph.D. ; Franco Taroni, Ph.D.
  Journal: Journal of Forensic Sciences  Volume:57  Issue:5  Dated:September 2012  Pages:1199 to 1216
  Date Published: 09/2012
  Page Count: 18
  Annotation: Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions.
  Abstract: Forensic scientists face increasingly complex inference problems for evaluating likelihood ratios (LRs) for an appropriate pair of propositions. Up to now, scientists and statisticians have derived LR formulae using an algebraic approach. However, this approach reaches its limits when addressing cases with an increasing number of variables and dependence relationships between these variables. In this study, we suggest using a graphical approach, based on the construction of Bayesian networks (BNs). The authors first construct a BN that captures the problem, and then deduce the expression for calculating the LR from this model to compare it with existing LR formulae. The authors illustrate this idea by applying it to the evaluation of an activity level LR in the context of the two-trace transfer problem. Their approach allows them to relax assumptions made in previous LR developments, produce a new LR formula for the two-trace transfer problem and generalize this scenario to n traces. Abstract published by arrangement with John Wiley & Sons.
  Main Term(s): Forensics/Forensic Sciences
  Index Term(s): Trace evidence ; Modeling techniques ; Blood/body fluid analysis ; Homicide investigations ; Scientific techniques ; DNA fingerprinting
  Type: Report (Technical)
  Country: United States of America
  Language: English
   
  To cite this abstract, use the following link:
https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=264203

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