Bayes' theorem describes how an observation should be transformed into evidence that updates a prior:
P(A|B) = P(B|A) P(A) / P(B)
Where A is a hypothesis, B is an observed event, P(A|B) is the posterior probability of the hypothesis conditioned on the observation, P(B|A) is the probability of the observation if the hypothesis were true, P(A) is the prior probability of the hypothesis being true, and P(B) is the prior probability of observing the event.