Abstract
Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to first-order logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequency-induced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.
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Williamson, J. Objective Bayesianism with predicate languages. Synthese 163, 341–356 (2008). https://doi.org/10.1007/s11229-007-9298-y
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DOI: https://doi.org/10.1007/s11229-007-9298-y