title: Combining a continuous Bayesian approach with grouping information.
abstract: When someone breaks glass a number of tiny fragments may be transferred to that person. If the glass is broken in the commission of a crime then these fragments may be used as evidence. A Bayesian interpretation of this evidence relies on, among other things, the forensic scientist's ability to assess the likelihood that the glass recovered from the suspect may have come from more than one source. This paper will examine the effect of including this information in the interpretation. We envisage working towards a system whereby the information loss that occurs during the normal casework activities of sample selection and glass fragment grouping is quantified.
authors.affiliation: Department of Statistics, University of Auckland, New Zealand. curran@stat.auckland.ac.nz
authors.forename: J M
authors.initials: JM
authors.lastname: Curran
authors.forename: C M
authors.initials: CM
authors.lastname: Triggs
authors.forename: J
authors.initials: J
authors.lastname: Buckleton
authors.forename: S
authors.initials: S
authors.lastname: Coulson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005554:Forensic Medicine / Q000379:methods*; D005898:Glass; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D009010:Monte Carlo Method; D018401:Sample Size