Indirect Evaluation by Simulation of a Bayesian Network
2014 (English)Conference paper, Poster (Other academic)
Evidence evaluation when addressing source level propositions is usually done by comparing a piece of recovered material from (specimens of) control material. When the control material source is not available for taking specimens or for investigating it in its entirety, we must stick to photographs or video take-ups for making comparisons. An example is the comparison of class characteristics between a recovered footwear print and a picture of a seized shoe, where the evaluation is occasionally made that way. However, this way of pursuing the investigation is due to needs of quick answers, when there is no or little time to send in the entire footwear for the comparison. Moreover, the pictures taken of the sole of the seized footwear are taken by the police under controlled conditions and with high quality equipment.
When the suspected source is captured on a lower quality video take-up and the recovered material consists of fragments from the original body of material – for instance fire debris – the comparison with the control material source is naturally more difficult. In this paper we present a case where the question is whether recovered fire debris originate from a piece of garment captured on a CCTV take-up. We show how a likelihood ratio for the two propositions can be indirectly obtained from a classification of the source of the fire debris, by using a Bayesian network model. Results from fire debris analysis as well as the results from image comparisons can be evaluated against propositions of class and the updating of the class node for fire debris propagates back to the propositions for source.
Feeding the network with uniform priors for the class nodes we show how simulation can be used to obtain the correct level of the likelihood ratio for further reporting.
Place, publisher, year, edition, pages
Bayesian networks, trace evidence, digital evidence
Probability Theory and Statistics Law and Society
IdentifiersURN: urn:nbn:se:liu:diva-118548OAI: oai:DiVA.org:liu-118548DiVA: diva2:815466
9th International Conference on Forensic Inference and Statistics (ICFIS2014)