Journal of Philosophical Logic 43 (2-3):283-302 (2014)

Paul D. Thorn
Heinrich Heine University Düsseldorf
The applicability of Bayesian conditionalization in setting one’s posterior probability for a proposition, α, is limited to cases where the value of a corresponding prior probability, PPRI(α|∧E), is available, where ∧E represents one’s complete body of evidence. In order to extend probability updating to cases where the prior probabilities needed for Bayesian conditionalization are unavailable, I introduce an inference schema, defeasible conditionalization, which allows one to update one’s personal probability in a proposition by conditioning on a proposition that represents a proper subset of one’s complete body of evidence. While defeasible conditionalization has wider applicability than standard Bayesian conditionalization (since it may be used when the value of a relevant prior probability, PPRI(α|∧E), is unavailable), there are circumstances under which some instances of defeasible conditionalization are unreasonable. To address this difficulty, I outline the conditions under which instances of defeasible conditionalization are defeated. To conclude the article, I suggest that the prescriptions of direct inference and statistical induction can be encoded within the proposed system of probability updating, by the selection of intuitively reasonable prior probabilities
Keywords Conditionalization  Probability updating  Principle of total evidence  Defeasible inference  Direct inference  Induction
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Reprint years 2014
DOI 10.1007/s10992-012-9263-1
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Logical Foundations of Probability.Rudolf Carnap - 1950 - Chicago]University of Chicago Press.
The Logic of Decision.Richard C. Jeffrey - 1965 - University of Chicago Press.
Laws and Symmetry.Bas C. van Fraassen - 1989 - Oxford University Press.

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