Abstract
The discharging of a gun results in the formation of extremely small particles known as gunshot residues (GSR). These may be deposited on the skin and clothing of the shooter, on other persons present, and on nearby items or surfaces. Several factors and their complex interactions affect the number of detectable GSR particles, which can deeply influence the conclusions drawn from likelihood ratios or posterior probabilities for prosecution hypotheses of interest. We present Bayesian network models for casework examples and demonstrate that probabilistic quantification of GSR evidence can be very sensitive to the assumptions concerning the model structure, prior probabilities, and the likelihood components. This finding has considerable implications for the use of statistical quantification of GSR evidence in the legal process.
| Original language | English |
|---|---|
| Pages (from-to) | 1149-1155 |
| Number of pages | 7 |
| Journal | Journal of Forensic Sciences |
| Volume | 58 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Sep 2013 |
Fingerprint
Keywords
- Bayesian networks
- Contamination
- Forensic science
- Gunshot residue
- Model uncertainty
- Statistical quantification of evidence
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Genetics
Cite this
Statistical challenges in the quantification of gunshot residue evidence. / Gauriot, Romain; Gunaratnam, Lawrence; Moroni, Rossana; Reinikainen, Tapani; Corander, Jukka.
In: Journal of Forensic Sciences, Vol. 58, No. 5, 09.2013, p. 1149-1155.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Statistical challenges in the quantification of gunshot residue evidence
AU - Gauriot, Romain
AU - Gunaratnam, Lawrence
AU - Moroni, Rossana
AU - Reinikainen, Tapani
AU - Corander, Jukka
PY - 2013/9
Y1 - 2013/9
N2 - The discharging of a gun results in the formation of extremely small particles known as gunshot residues (GSR). These may be deposited on the skin and clothing of the shooter, on other persons present, and on nearby items or surfaces. Several factors and their complex interactions affect the number of detectable GSR particles, which can deeply influence the conclusions drawn from likelihood ratios or posterior probabilities for prosecution hypotheses of interest. We present Bayesian network models for casework examples and demonstrate that probabilistic quantification of GSR evidence can be very sensitive to the assumptions concerning the model structure, prior probabilities, and the likelihood components. This finding has considerable implications for the use of statistical quantification of GSR evidence in the legal process.
AB - The discharging of a gun results in the formation of extremely small particles known as gunshot residues (GSR). These may be deposited on the skin and clothing of the shooter, on other persons present, and on nearby items or surfaces. Several factors and their complex interactions affect the number of detectable GSR particles, which can deeply influence the conclusions drawn from likelihood ratios or posterior probabilities for prosecution hypotheses of interest. We present Bayesian network models for casework examples and demonstrate that probabilistic quantification of GSR evidence can be very sensitive to the assumptions concerning the model structure, prior probabilities, and the likelihood components. This finding has considerable implications for the use of statistical quantification of GSR evidence in the legal process.
KW - Bayesian networks
KW - Contamination
KW - Forensic science
KW - Gunshot residue
KW - Model uncertainty
KW - Statistical quantification of evidence
UR - http://www.scopus.com/inward/record.url?scp=84883552025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883552025&partnerID=8YFLogxK
U2 - 10.1111/1556-4029.12179
DO - 10.1111/1556-4029.12179
M3 - Article
C2 - 23822522
AN - SCOPUS:84883552025
VL - 58
SP - 1149
EP - 1155
JO - Journal of Forensic Sciences
JF - Journal of Forensic Sciences
SN - 0022-1198
IS - 5
ER -