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    Fiducial probability can give classical statistics an epi... — Carmelics
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    Fiducial probability can give classical statistics an epistemic status

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    1 reason for
    2 reasons against

    Reasons For

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    Reason for
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    • Fiducial probability derives a probability assignment over hypotheses without assuming a prior probability over statistical hypotheses at the outset
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    Reasons Against

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    Reason against 1 of 2
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    • 1.Fiducial distributions lack coherence: they can assign probabilities that violate the axioms of probability when applied to different parameterizations of the same model.
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    • 2.A probability assignment that fails coherence cannot legitimately ground epistemic credence in hypotheses, as Lindley demonstrated via fiducial contradictions in the 1950s.
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    Reason against 2 of 2
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    • 1.Fisher's fiducial argument illicitly converts a frequency statement about estimators into a posterior-style statement about parameters without epistemic justification.
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    • 2.The logical gap between 'the procedure traps the true value 95% of the time' and '95% probability the parameter lies here' requires exactly the prior-free inference fiducial claims to achieve, making the derivation circular.
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    A probability assignment that fails coherence cannot legitimately ground epistem...Fiducial distributions lack coherence: they can assign probabilities that violat...Fiducial probability derives a probability assignment over hypotheses without as...Fisher's fiducial argument illicitly converts a frequency statement about estima...
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    The logical gap between 'the procedure traps the true value 95% of the time' and...

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    Fiducial probability presents another way in which classical statistics can be given an epistemic status. Fisher (1930, 1933, 1935c, 1956/1973) developed the notion of fiducial probability as a way of deriving a probability assignment over hypotheses without assuming a prior probability over statistical hypotheses at the outset. The fiducial argument is controversial, and it is generally agreed that its applicability is limited to particular statistical problems. Dempster (1964), Hacking (1965),
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    Details

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    claim
    Perspectives
    3 (1 for, 2 against)
    Edits
    1 edit