Bayesian updating is a statistical method that involves revising the probability estimate for a hypothesis as more evidence or information becomes available. It combines prior beliefs and new evidence to generate a posterior probability, which reflects a more informed view of the likelihood of the hypothesis being true. This approach is fundamental in Bayesian decision theory, as it allows for continuous learning and adaptation based on incoming data.
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