Bayes theorem states the following:

*Posterior = Prior * Likelihood*

This can also be stated as *P (A | B) = (P (B | A) * P(A)) / P(B)* , where *P(A|B)* is the probability of *A* given *B*, also called posterior.

**Prior**: Probability distribution representing knowledge or uncertainty of a data object prior or before observing it

**Posterior**: Conditional probability distribution representing what parameters are likely after observing the data object

**Likelihood**: The probability of falling under a specific category or class.

This is represented as follows: