Bayesian estimation is a statistical method that applies Bayes' theorem to update the probability estimate for a hypothesis as more evidence or information becomes available. It blends prior knowledge with observed data to produce a posterior distribution, which captures the uncertainty surrounding the estimation process. This approach stands out because it allows for continuous updating and incorporates prior beliefs, making it adaptable in various contexts such as point estimation, regression analysis, and hypothesis testing.
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