Maximum a posteriori estimation (MAP) is a statistical technique used to estimate an unknown parameter by maximizing the posterior distribution, which combines prior beliefs with observed data. This method is particularly important in machine learning and probabilistic models because it allows practitioners to incorporate prior information about parameters, leading to more informed estimates when data is limited or noisy. MAP is a powerful tool for decision-making in uncertain environments.
congrats on reading the definition of maximum a posteriori estimation. now let's actually learn it.