Justifying Objective Bayesianism on Predicate Languages

Entropy

Jürgen Landes¹  Jon Williamson² 

[1] Department of Philosophy, School of European Culture and Languages, University of Kent, Canterbury CT2 7NF, UK; E-Mail

关键词: objective Bayesianism;    g-entropy;    predicate language;    scoring rule;    minimax;   

DOI  :  10.3390/e17042459

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland