Bayesian philosophy and Bayesian statistics have diverged in recent years, because Bayesian philosophers have become more interested in philosophical problems other than the foundations of statistics and Bayesian statisticians have become less concerned with philosophical foundations. One way in which this divergence manifests itself is through the use of direct inference principles: Bayesian philosophers routinely advocate principles that require calibration of degrees of belief to available non-epistemic probabilities, while Bayesian statisticians rarely invoke such principles. As I explain, however, the standard Bayesian framework cannot coherently employ direct inference principles. Direct inference requires a shift towards a non-standard Bayesian framework, which further increases the gap between Bayesian philosophy and Bayesian statistics. This divergence does not preclude the application of Bayesian philosophical methods to real-world problems. Data consolidation is a key challenge for present-day systems medicine and other systems sciences. I show that data consolidation requires direct inference and that the non-standard Bayesian methods outlined here are well suited to this task.

中文翻译：

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哲学角度的贝叶斯主义及其在医学中的应用

近年来，贝叶斯哲学和贝叶斯统计学出现了分歧，因为贝叶斯哲学家对统计学基础以外的哲学问题变得更感兴趣，而贝叶斯统计学家对哲学基础越来越不关心。这种分歧表现出来的一种方式是通过使用直接推理原则：贝叶斯哲学家通常提倡需要根据可用的非认知概率来校准信念程度的原则，而贝叶斯统计学家很少援引这些原则。然而，正如我所解释的，标准贝叶斯框架无法连贯地采用直接推理原则。直接推理需要转向非标准贝叶斯框架，这进一步拉大了贝叶斯哲学和贝叶斯统计之间的差距。这种分歧并不妨碍贝叶斯哲学方法应用于现实世界的问题。数据整合是当今系统医学和其他系统科学面临的主要挑战。我表明数据整合需要直接推理，并且此处概述的非标准贝叶斯方法非常适合此任务。