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  1. Commutativity or Holism? A Dilemma for Conditionalizers.Jonathan Weisberg - 2009 - British Journal for the Philosophy of Science 60 (4):793-812.
    Conditionalization and Jeffrey Conditionalization cannot simultaneously satisfy two widely held desiderata on rules for empirical learning. The first desideratum is confirmational holism, which says that the evidential import of an experience is always sensitive to our background assumptions. The second desideratum is commutativity, which says that the order in which one acquires evidence shouldn't affect what conclusions one draws, provided the same total evidence is gathered in the end. (Jeffrey) Conditionalization cannot satisfy either of these desiderata without violating the other. (...)
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  • General properties of general Bayesian learning.Miklós Rédei & Zalán Gyenis - unknown
    We investigate the general properties of general Bayesian learning, where ``general Bayesian learning'' means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability measure. If (...)
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  • Bayesian conditionalization and probability kinematics.Colin Howson & Allan Franklin - 1994 - British Journal for the Philosophy of Science 45 (2):451-466.
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  • Radical probabilism and bayesian conditioning.Richard Bradley - 2005 - Philosophy of Science 72 (2):342-364.
    Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves one’s (...)
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