  One formulation of forensic identification of source problems is to determine
the source of trace evidence, for instance, glass fragments found on a suspect
for a crime. The current state of the science is to compute a Bayes factor (BF)
comparing the marginal distribution of measurements of trace evidence under two
competing propositions for whether or not the unknown source evidence
originated from a specific source. The obvious problem with such an approach is
the ability to tailor the prior distributions (placed on the
features/parameters of the statistical model for the measurements of trace
evidence) in favor of the defense or prosecution, which is further complicated
by the fact that the typical number of measurements of trace evidence is
typically sufficiently small that prior choice/specification has a strong
influence on the value of the BF. To remedy this problem of prior specification
and choice, we develop an alternative to the BF, within the framework of
generalized fiducial inference (GFI), that we term a {\em generalized fiducial
factor} (GFF). Furthermore, we demonstrate empirically, on the synthetic and
real Netherlands Forensic Institute (NFI) casework data, deficiencies in the BF
and classical/frequentist likelihood ratio (LR) approaches.
