Jeffreys Prior is a non-informative prior distribution used in Bayesian statistics that is based on the concept of invariant measures under reparameterization. It provides a way to assign prior probabilities in a manner that reflects the underlying parameter space without imposing strong subjective beliefs. This prior is particularly useful when there is little or no prior information available about the parameters being estimated, making it widely applicable in various statistical models.
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