Justifying Objective Bayesianism on Predicate Languages

Landes, Jürgen and Williamson, Jon (22. April 2015): Justifying Objective Bayesianism on Predicate Languages. In: Entropy, Vol. 17, No. 4: pp. 2459-2543

Abstract

Objective 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.

• Item Type: Journal article
• Form of publication: Postprint
• Keywords: objective Bayesianism; g-entropy; predicate language; scoring rule; minimax
• Faculties: Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP); Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) > Logic; Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) > Epistemology
• Subjects: 100 Philosophy and Psychology > 120 Epistemology; 100 Philosophy and Psychology > 160 Logic
• ISSN: 1099-4300
• Language: English