Maximum a posteriori (MAP) estimation refers to the method of estimating an unknown parameter by maximizing the posterior distribution. This approach combines prior knowledge about the parameter with the observed data, resulting in a more informed estimation. MAP is particularly important in Bayesian statistics, where it helps in obtaining point estimates while incorporating prior beliefs and evidence from data.
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