Posterior
In Bayesian statistics, the updated probability distribution of a parameter or hypothesis after observing new evidence or training data.
Think of It Like This
Like your revised opinion of a restaurant's quality after you actually eat a meal there, combining your prior expectations with the new experience.
The posterior is calculated by multiplying the prior probability by the likelihood of the observed data, normalized by the evidence. It represents the final output of Bayesian inference. In machine learning, MAP (Maximum A Posteriori) estimation seeks to find the single most probable point in this posterior distribution.