Bayesian inference is one way prediction can be formalized. It combines an estimation of the prior probability that an event will take place and an assessment of the likelihood of new data to give a new updated estimate of the posterior probability of the event. Important concepts in Bayesian inference are rational analysis, the notions of optimal inference or an ideal observer, and that processing can be corrupted in a noisy channel.