Statistical evaluation of forensic traces at activity level

Consider a case where a person, the suspect, broke a shop window and stole items. The suspect has been apprehended a couple of hours later and his garments has been seized. Glass fragment has been recovered from his garments and are of the same type as the glass from the window. The suspect has been charged with the robbery. One of the issues that the court in interested in is whether the suspect broke the window. This can be achieved by addressing a pair of hypotheses:

H1: The suspect broke the window

H2: The suspect did not break the window

The evidence consists of the glass recovered from the suspect’s garment, y, and the glass recovered from the broken window, x. The statistical evaluation of this evidence is achieved through a likelihood ratio (LR) calculated using, 

LR=Pr(x,y|H1)/Pr(x,y|H2)LR                                                    (1)

The probability calculations require statistical models for glass transfer and persistence on the garment after two hours, the recovery of the fragments, and background glass in garments. One aim is to develop statistical models for these aspect for forensic traces in general. 

LRCFS has created datasets addressing transfer and persistence of fibres and  has also created a universal experimental protocol for investigating transfer and persistence of traces. These datasets can be used to investigate generative statistical models that incorporates transfer and persistence components.

Diversity statement

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research.  We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.