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    Home/Original/inverse
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    Inverse View

    It is not the case that Likelihoodists, who reject Bayesian prior probabilities, may still embrace the Likelihood Ratio Convergence Theorem.

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    2 perspectives
    Reason for 1 of 2
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    • 1.Likelihoods themselves presuppose a sampling model whose selection reflects implicit prior commitments about which hypotheses are worth comparing.
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    • 2.The Likelihood Ratio Convergence Theorem's practical application requires specifying a hypothesis space, and delimiting that space embeds prior probabilistic judgments.
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    • 3.A theorem that is prior-free in its formal statement can still require prior-laden choices to generate the likelihoods it operates on, as Deborah Mayo's error-statistical critique of Bayesianism implies.
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    Reason for 2 of 2
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    • 1.The Likelihood Ratio Convergence Theorem guarantees convergence only given that the true hypothesis is included in the comparison set.
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    • 2.Selecting which hypotheses to include in a likelihood ratio comparison is not itself licensed by likelihoods alone, requiring a prior probability-like commitment.
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    • 3.Ian Hacking's early likelihoodism acknowledged this gap, conceding that likelihoods cannot adjudicate the choice of the hypothesis space itself.
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    Reasons Against

    1 perspective
    Reason against
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    • 1.The Likelihood Ratio Convergence Theorem draws only on likelihoods.
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    • 2.The statement and proof of the Likelihood Ratio Convergence Theorem do not employ prior probabilities of any kind.
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