Let our parameter of interest be \theta. As computing Jeffreys prior makes use of the Fisher information I(\theta ), it is somehow related to the frequentist MLE approach (which has variance I(\theta )^{-1}). This yields interpretations of Jeffreys prior in terms of frequentist notions of estimation, uncertainty, and information.
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The Jeffreys prior gives more weight to values of \theta whose MLE estimate has ____ uncertainty.
As a result, the Jeffreys prior yields more weight to values of \theta where the data has ____ information towards deciding the parameter.
The Fisher information can be taken as a proxy for how much, at a particular parameter value \theta, would equivalent shifts to the parameter influence the data. Thus, Jeffreys prior gives more weight to regions where the potential outcomes are ____ sensitive to slight changes in \theta.
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