Prior probability refers to the initial assessment of the likelihood of an event occurring before new evidence is considered. It serves as a baseline for updating beliefs or probabilities when new data is introduced, making it a fundamental component in Bayesian inference and decision-making processes. Understanding prior probabilities is essential for effectively applying Bayes' theorem, where they are combined with observed evidence to yield posterior probabilities.
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