  When presenting forensic evidence, such as a DNA match, experts often use the
Likelihood ratio (LR) to explain the impact of evidence . The LR measures the
probative value of the evidence with respect to a single hypothesis such as
'DNA comes from the suspect', and is defined as the probability of the evidence
if the hypothesis is true divided by the probability of the evidence if the
hypothesis is false. The LR is a valid measure of probative value because, by
Bayes Theorem, the higher the LR is, the more our belief in the probability the
hypothesis is true increases after observing the evidence. The LR is popular
because it measures the probative value of evidence without having to make any
explicit assumptions about the prior probability of the hypothesis. However,
whereas the LR can in principle be easily calculated for a distinct single
piece of evidence that relates directly to a specific hypothesis, in most
realistic situations 'the evidence' is made up of multiple dependent components
that impact multiple different hypotheses. In such situations the LR cannot be
calculated . However, once the multiple pieces of evidence and hypotheses are
modelled as a causal Bayesian network (BN), any relevant LR can be
automatically derived using any BN software application.
