 \(E\) represents a proposition about accepted evidence, \(H\) stands for a hypothesis, \(K\) for background knowledge and \(Pr\) for the agent’s subjective probability function. To confirm \(H\) is to raise its conditional probability, relative to \(K\). The shift from prior probability \(Pr(H \mid K)\) to posterior probability \(Pr(H \mid E \cdot K)\) is referred to as conditionalization on \(E\). The relation between these two probabilities is typically given by Bayes’ Theorem (setting aside more complex forms of conditionalization):