Adapted from Bayes’ Rule, the basic setup of Bayesian inference is:

where is the posterior, or the distribution of the parameter updated after observing data X. is the likelihood of the observed data

is the prior distribution assigned to based on a subjective degree of beliefP(X) is the marginal distribution of X that normalizes the posterior into a valid probability distribution