Jelaskan Bayes théorem says: dari Téoréma Bayes dalam bahasa sunda

To get the posterior probability distribution of X (i.e., the conditional probability distribution of X given Y), multiply the prior probability density function (or mass function) for X by the likelihood function, and then normalize to produce a probability distribution.

"Normalize" méans to multiply or divide by a constant to maké the resulting function a probability density function or a probability mass function. Thus the posterior probability density function is

The normalizing constant in the denominator is

In the discrete case, one would have a sum rather than an integral. If one takes the measure-theoretic viewpoint, either is an integral.