TY - JOUR
T1 - A general approach to Bayesian networks for the interpretation of evidence
AU - Taroni, F
AU - Biedermann, A
AU - Garbolino, P
AU - Aitken, C G G
PY - 2004/1
Y1 - 2004/1
N2 - Bayesian networks (BNs) are mathematically and statistically rigorous techniques for handling uncertainty. The field of forensic science has recently attributed increased attention to the many advantages of this graphical method for assisting the evaluation of scientific evidence. However, the majority of contributions that relate to this topic restrict themselves to the presentation of already "constructed" BNs, and often, only a few explanations are given as to how one obtains a specific BN structure for a given problem. Based on several examples, the present paper will therefore attempt to explain in more detail some guiding considerations that might be helpful for the elicitation of appropriate structures for BNs.
AB - Bayesian networks (BNs) are mathematically and statistically rigorous techniques for handling uncertainty. The field of forensic science has recently attributed increased attention to the many advantages of this graphical method for assisting the evaluation of scientific evidence. However, the majority of contributions that relate to this topic restrict themselves to the presentation of already "constructed" BNs, and often, only a few explanations are given as to how one obtains a specific BN structure for a given problem. Based on several examples, the present paper will therefore attempt to explain in more detail some guiding considerations that might be helpful for the elicitation of appropriate structures for BNs.
U2 - 10.1016/j.forsciint.2003.08.004
DO - 10.1016/j.forsciint.2003.08.004
M3 - Article
C2 - 14687767
SN - 0379-0738
VL - 139
SP - 5
EP - 16
JO - Forensic Science International
JF - Forensic Science International
IS - 1
ER -