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    Representation theorems equate a prior distribution over ... — Carmelics
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    Supports→The prior distribution over statistical hypotheses can be interpreted subjectively

    Representation theorems equate a prior distribution over statistical hypotheses to a prediction rule

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    Bayes' theorem expresses a necessary connection between the probabilit...81%The prior distribution over statistical hypotheses can be interpreted ...80%Fiducial probability derives a probability assignment over hypotheses ...79%The prior distribution over hypotheses faces an interpretation problem79%

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    Representation theorems equate a prior distribution over statistical hypotheses to a prediction rule, and thus to a probability assignment that can be given a subjective and behavioral interpretation. This removes the worry expressed above, that the prior distribution over hypotheses cannot be interpreted subjectively because it cannot be related to belief as a willingness to act: priors relate uniquely to particular predictions. However, for De Finetti the representation theorem provided a reas

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