  Logistic-regression calibration and fusion are potential steps in the
calculation of forensic likelihood ratios. The present paper provides a
tutorial on logistic-regression calibration and fusion at a practical
conceptual level with minimal mathematical complexity. A score is
log-likelihood-ratio like in that it indicates the degree of similarity of a
pair of samples while taking into consideration their typicality with respect
to a model of the relevant population. A higher-valued score provides more
support for the same-origin hypothesis over the different-origin hypothesis
than does a lower-valued score; however, the absolute values of scores are not
interpretable as log likelihood ratios. Logistic-regression calibration is a
procedure for converting scores to log likelihood ratios, and
logistic-regression fusion is a procedure for converting parallel sets of
scores from multiple forensic-comparison systems to log likelihood ratios.
Logistic-regression calibration and fusion were developed for automatic speaker
recognition and are popular in forensic voice comparison. They can also be
applied in other branches of forensic science, a fingerprint/fingermark example
is provided.
