Fiducial inference is a statistical framework developed by the mathematician Ronald A. Fisher in the early 20th century. It is intended for making inferences about parameters of a statistical model based on observed data without relying on the subjective probabilities associated with prior distributions, which are common in Bayesian statistics.
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