在统计学中，最大后验（英文为Maximum a posteriori，缩写为MAP）估计方法根据经验数据获得对难以观察的量的点估计。它与最大似然估计中的 Fisher 方法有密切关系，但是它使用了一个增大的优化目标，这种方法将被估计量的先验分布融合到其中。所以最大后验估计可以看作是规则化（regularization）的最大似然估计。

-In statistics, the maximum a posteriori (English as a Maximum a posteriori, abbreviated as MAP) estimation method according to empirical data is difficult to obtain right amount of observation point estimate. It is with the maximum likelihood estimation of the Fisher method is closely related to, but it uses a larger optimization goals, this approach would be the estimated amount of integration of prior distribution to it. Therefore, maximum a posteriori estimates can be regarded as regularization (regularization) of the maximum likelihood estimate.