MAP Estimation
Maximum A Posteriori estimation is a Bayesian method for finding the most likely model parameters by combining observed data with a prior distribution.
Think of It Like This
Like a detective who uses new crime scene evidence to update their strong initial hunch about who committed the crime.
Unlike Maximum Likelihood Estimation (MLE) which relies solely on the data, MAP incorporates prior beliefs. As the amount of data increases, the influence of the prior diminishes, and MAP converges toward MLE. In machine learning, MAP is mathematically equivalent to adding regularization (like L2) to a loss function.