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Entropy 2015, 17(4), 2459-2543; doi:10.3390/e17042459

Justifying Objective Bayesianism on Predicate Languages

Department of Philosophy, School of European Culture and Languages, University of Kent, Canterbury CT2 7NF, UK
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Academic Editor: Kevin H. Knuth
Received: 11 February 2015 / Revised: 27 March 2015 / Accepted: 9 April 2015 / Published: 22 April 2015
(This article belongs to the Special Issue Maximum Entropy Applied to Inductive Logic and Reasoning)
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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. View Full-Text
Keywords: objective Bayesianism; g-entropy; predicate language; scoring rule; minimax objective Bayesianism; g-entropy; predicate language; scoring rule; minimax
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Landes, J.; Williamson, J. Justifying Objective Bayesianism on Predicate Languages. Entropy 2015, 17, 2459-2543.

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