Prior probability is the initial assessment of the likelihood of an event occurring before new evidence is taken into account. It serves as a foundational concept in statistical inference, especially in Bayesian statistics, where it is combined with conditional probabilities to update beliefs based on observed data. Understanding prior probability is crucial for applying Bayes' theorem effectively, as it influences the resulting posterior probability and can significantly affect decision-making processes.
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