Map estimation, specifically Maximum a Posteriori (MAP) estimation, is a statistical method used to estimate an unknown quantity by maximizing the posterior distribution. This approach combines prior information about the parameter with the likelihood of observed data, resulting in a point estimate that reflects both the uncertainty of the data and any prior beliefs. MAP estimation is particularly useful in scenarios where data is sparse or noisy, providing a way to incorporate additional knowledge into the estimation process.
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