A Bayesian network for glass evidence evaluation at activity level: a novel approach to model the background distribution

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• Peter Vergeer (Netherlands Forensic Institute)
• Monday 26 September 2016, 16:15-17:00
• Seminar Room 1, Newton Institute.

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FOSW02 - Bayesian networks and argumentation in evidence analysis

In burglary cases the comparison of glass particles found on a piece of clothing of a suspect and a broken reference glass pane is of importance. Often, suspects are known as multiple offenders and may have a large collection of glass on their clothing. Therefore, in order to evaluate the strength of evidence, current likelihood ratio formulas contain parameters such as the number of groups of glass found on a piece of clothing, and the size of the matching group [1]. In order to obtain probabilities for these parameters, glass particles found on clothing of suspects have been counted and grouped, see e.g. [2]. In general, the amount of glass particles found on a suspect is limited in these studies.

A database of glass from suspects in the Netherlands shows quite deviant results. Up to a few hundred of glass particles are often encountered and only samples may be analyzed. In order to evaluate the evidential strength of a sample of particles with a background model based on samples from casework requires a different background model. We propose to model the background distribution of the sample by a ‘Chinese restaurant process’ [3].  

[1] Forensic Interpretation of Glass Evidence, CRC Press. (2000). https://www.crcpress.com/Forensic-Interpretation-of-Glass-Evidence/Curran-Hicks-Champod-Buckleton/97… (accessed April 19, 2016).
[2] J.A. Lambert, M.J. Satterthwaite, P.H. Harrison, A survey of glass fragments recovered from clothing of persons suspected of involvement in crime, Sci. Justice. 35 (1995) 273–281. doi:10.1016/S1355-0306(95)72681-8.
[3] D.J. Aldous, Exchangeability and related topics, in: P.L. Hennequin (Ed.), Éc. DÉté Probab. St.-Flour XIII — 1983, Springer Berlin Heidelberg, 1985: pp. 1–198. http://link.springer.com/chapter/10.1007/BFb0099421 (accessed September 2, 2016).

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