Maximum a posteriori (MAP) estimation is a Bayesian method that identifies the mode of the posterior distribution as the best estimate of a parameter. This approach incorporates both prior beliefs about the parameter and the evidence from observed data, balancing these two sources of information to provide a more informed estimate. By finding the point where the posterior distribution reaches its highest value, MAP estimation helps in making decisions under uncertainty, linking it closely to Bayesian estimation and decision-making frameworks.
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