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