 The idea of the Bayesian approach is to assign probabilities not only to the events which constitute evidence, but also to hypotheses. One starts with a “prior probability” distribution over the relevant hypotheses \(p(H)\). On learning some evidence E, the Bayesian updates the prior \(p(H)\) to the conditional probability \(p(H\mid E)\). This update rule is called the “rule of conditionalisation”. The conditional probability \(p(H\mid E)\) is known as the “posterior probability”, and is calculated using Bayes’ rule: