Prior distributions represent the initial beliefs or information about a parameter before observing any data. They play a critical role in Bayesian statistics, as they are combined with the likelihood of observed data using Bayes' theorem to produce a posterior distribution, which updates our beliefs based on new evidence. Understanding prior distributions is essential for interpreting results and making informed decisions in statistical inference.
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