 sometimes called the Bayes  factor.[1]  This is the ratio between the probability \(\Pr(E \pmid H)\) of observing evidence \(E\) assuming \(H\) (often called the likelihood) and the probability \(\Pr(E)\) of observing \(E\). By the law of total probability, \(\Pr(E)\) results from adding the probability of observing \(E\) assuming \(H\) and the probability of observing \(E\) assuming \(\neg H\), each weighted by the prior probabilities of \(H\) and \(\neg H\) respectively: