Maximum A Posteriori Estimation (MAP)

MAP estimation extends maximum likelihood with Bayesian reasoning. Instead of choosing only the parameters that best explain the data, it also prefers parameters that are consistent with prior knowledge. This is especially useful when data is limited or when we have meaningful prior beliefs about parameter values. In machine learning, MAP is also closely related to regularization; some regularization terms can be interpreted as Bayesian priors. For that reason, MAP is a powerful estimation approach that bridges probability and domain knowledge.