The posterior mean is a central concept in Bayesian statistics, representing the expected value of a parameter after observing data. It combines prior beliefs about the parameter with the likelihood of the observed data, producing an updated estimate. This measure reflects the average of the parameter's distribution after incorporating new evidence, making it essential for decision-making and inference in uncertain situations.
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