Prior beliefs refer to the initial assumptions or opinions that individuals hold about a particular parameter or hypothesis before observing any data. These beliefs play a critical role in Bayesian statistics, as they are combined with new evidence to update our understanding and form posterior beliefs, shaping the final conclusions we draw from the data.
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