@article{ref1,
title="Tutorial on logistic-regression calibration and fusion: converting a score to a likelihood ratio",
journal="Australian journal of forensic sciences",
year="2013",
author="Morrison, Geoffrey Stewart",
volume="45",
number="2",
pages="173-197",
abstract="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/finger-mark example is provided.<p /> <p>Language: en</p>",
language="en",
issn="0045-0618",
doi="10.1080/00450618.2012.733025",
url="http://dx.doi.org/10.1080/00450618.2012.733025"
}