Prior probability refers to the initial assessment of the likelihood of an event occurring before new evidence is taken into account. It serves as the foundation for Bayesian inference, where this initial belief is updated with new information to form a posterior probability. Understanding prior probability is essential in applying Bayes' theorem, as it influences the overall outcome of probability calculations and helps in decision-making processes under uncertainty.
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