The posterior mean is the expected value of a parameter given the observed data and prior information, calculated within the Bayesian framework. This concept combines the likelihood of the data under a specific parameter with the prior distribution of that parameter, resulting in an updated estimate after considering new evidence. It serves as a point estimate of the parameter and is particularly important in making predictions and decisions based on uncertain information.
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