Prior, likelihood, and posterior

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: