Jeffreys Prior is a non-informative prior distribution used in Bayesian statistics that is based on the likelihood function and is invariant under reparameterization. It is particularly useful when there is limited prior information available about the parameter of interest. This prior has a strong theoretical foundation and provides a way to express uncertainty in a principled manner, often leading to more robust posterior distributions when combined with observed data.
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