A Two-Level Model for Evidence Evaluation in the Presence of Zeros

Gregorgz Zadora, Tereza Neocleous, Colin Aitken

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Likelihood ratios (LRs) provide a natural way of computing the value of evidence under competing propositions. We propose LR models for classification and comparison that extend the ideas of Aitken, Zadora, and Lucy and Aitken and Lucy to include consideration of zeros. Instead of substituting zeros by a small value, we view the presence of zeros as informative and model it using Bernoulli distributions. The proposed models are used for evaluation of forensic glass (comparison and classification problem) and paint data (comparison problem). Two hundred and sixty-four glass samples were analyzed by scanning electron microscopy, coupled with an energy dispersive X-ray spectrometer method and 36 acrylic topcoat paint samples by pyrolysis gas chromatography hyphened with mass spectrometer method. The proposed LR model gave very satisfactory results for the glass comparison problem and for most of the classification tasks for glass. Results of comparison of paints were also highly satisfactory, with only 3.0% false positive answers and 2.8% false negative answers.
Original languageEnglish
Pages (from-to)371-384
Number of pages14
JournalJournal of Forensic Sciences
Issue number2
Publication statusPublished - Mar 2010


  • forensic science
  • evidence evaluation
  • likelihood ratio
  • missing data
  • multivariate data
  • graphical models

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