Justifying Objective Bayesianism on Predicate Languages
AbstractObjective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the maximum entropy principle. In this paper we investigate the extent to which one can provide a unified justification of the objective Bayesian norms in the case in which the background language is a first-order predicate language, with a view to applying the resulting formalism to inductive logic. We show that the maximum entropy principle can be motivated largely in terms of minimising worst-case expected loss. View Full-Text
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Landes, J.; Williamson, J. Justifying Objective Bayesianism on Predicate Languages. Entropy 2015, 17, 2459-2543.
Landes J, Williamson J. Justifying Objective Bayesianism on Predicate Languages. Entropy. 2015; 17(4):2459-2543.Chicago/Turabian Style
Landes, Jürgen; Williamson, Jon. 2015. "Justifying Objective Bayesianism on Predicate Languages." Entropy 17, no. 4: 2459-2543.