The posterior distribution is a probability distribution that represents the updated beliefs about a parameter after observing new data. It combines prior knowledge with the likelihood of the observed data, following Bayes' theorem. This concept is crucial for Bayesian estimation, as it allows for making inferences about unknown parameters based on both prior information and observed evidence.
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