Justifying Objective Bayesianism on Predicate Languages
Justifying Objective Bayesianism on Predicate Languages
复制标题
证明谓词语言上的客观贝叶斯主义的合理性
DOI：
10.3390/e17042459
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发表时间：
2015
期刊：
影响因子：
2.7
通讯作者：
Jon Williamson
中科院分区：
文献类型：
--
作者：
J. Landes;Jon Williamson
客观贝叶斯主义认为，一个人的信念的力量应该是概率，只要有证据，就应该根据物理概率进行校准，否则就足够模棱两可。这些信念规范通常使用最大熵原理来解释。在本文中，我们调查在何种程度上可以提供一个统一的理由的情况下，背景语言是一阶谓词语言的客观贝叶斯规范，以期将由此产生的形式主义归纳逻辑。我们表明，最大熵原理的动机在很大程度上可以在最大限度地减少最坏情况下的预期损失。
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.