Justifying Objective Bayesianism on Predicate Languages

Landes, Jürgen and Williamson, Jon (2015) Justifying Objective Bayesianism on Predicate Languages. Entropy, 17 (4). pp. 2459-2543. ISSN 1099-4300. (doi:https://doi.org/10.3390/e17042459) (Full text available)

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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: Article
Subjects: B Philosophy. Psychology. Religion > B Philosophy (General)
B Philosophy. Psychology. Religion > BC Logic
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
Divisions: Faculties > Humanities > School of European Culture and Languages > Philosophy
Depositing User: Jon Williamson
Date Deposited: 07 May 2015 15:30 UTC
Last Modified: 27 Feb 2017 09:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48289 (The current URI for this page, for reference purposes)
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