The posterior mean is the expected value of a parameter given the observed data and prior information, representing a central tendency in Bayesian statistics. It is calculated by taking the average of the parameter estimates after incorporating the likelihood of the observed data with the prior distribution. This concept highlights the importance of updating beliefs based on new evidence, providing a powerful tool for inference in various applications.
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