In Bayesian statistics, a maximum posterior probability estimate is an estimate of an unknown quantity that is equal to the mode of the posterior distribution.
The MAP (Maximum A-posteriori) can be used to obtain a point estimate of an unobserved quantity based on empirical data.
It is closely related to the maximum likelihood estimation method, but uses an augmented optimization objective that incorporates a prior distribution on the quantity to be estimated.
The MAP (Maximum A-posteriori) estimate can therefore be seen as a regularization of the maximum likelihood estimate.
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