We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as "uncertain evidence." We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables. We consider a recently-proposed method "distributional evidence" as well as revisit two older methods: Jeffrey's rule and virtual evidence. We devise guidelines on how to account for uncertain evidence and we provide new insights, particularly regarding consistency. To showcase the impact of different interpretations of the same uncertain evidence, we carry out experiments in which one interpretation is defined as "correct." We then compare inference results from each different interpretation illustrating the importance of careful consideration of uncertain evidence.

翻译：我们考虑在概率模型中进行贝叶斯推理的问题，其中观测结果伴随不确定性，即所谓的“不确定性证据”。我们探讨如何解读不确定性证据，并进一步阐明正确解读对潜在变量推理的重要性。我们研究了一种近期提出的方法“分布证据”，并重新审视了两种经典方法：杰弗里规则与虚拟证据。我们制定了处理不确定性证据的指导原则，并提供了新的见解，特别是在一致性方面。为了展示同一不确定性证据不同解读方式的影响，我们进行了实验，其中一种解读被定义为“正确”。然后，我们比较了每种不同解读方式的推理结果，以说明谨慎处理不确定性证据的重要性。