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    Harold Jeffreys developed a fully Bayesian inductive logi... — Carmelics
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    Supports→The term 'Bayesian inductive logic' carrying the connotation of purely subjective probabilities is misleading.

    Harold Jeffreys developed a fully Bayesian inductive logic using objective, invariant priors derived from the Fisher information metric, not personal credences.

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    Reasons For

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    Reason for
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    • 1.Fisher information is coordinate-invariant, providing an objective mathematical basis for priors independent of parameterization choices.
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    • 2.Jeffreys priors produce consistent inductive behavior across equivalent statistical models, satisfying rational requirements for logical consistency.
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    • 3.Deriving priors from information geometry avoids arbitrary credence assignments, making inductive logic more principled and reproducible.
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    Reasons Against

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    • 1.Mathematical objectivity (invariance) doesn't establish epistemic objectivity—it may just encode hidden choices about what geometry matters.
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    • 2.Jeffreys priors often produce paradoxical results (e.g., infinite-dimensional limits, model selection issues) that undermine their foundational status.
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    • 3.No prior genuinely escapes subjectivity; Fisher-based priors encode substantive assumptions about model structure and information measurement.
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    Truth & Knowledge1 linkedPhilosophy of Language1 linked

    Related

    Deriving priors from information geometry avoids arbitrary credence assignments,...Fisher information is coordinate-invariant, providing an objective mathematical ...Jeffreys priors often produce paradoxical results (e.g., infinite-dimensional li...Jeffreys priors produce consistent inductive behavior across equivalent statisti...
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    Mathematical objectivity (invariance) doesn't establish epistemic objectivity—it...No prior genuinely escapes subjectivity; Fisher-based priors encode substantive ...The term 'Bayesian inductive logic' carrying the connotation of purely subjectiv...

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