We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as `uncertain evidence'. In many real-world scenarios, such uncertainty stems from measurement errors associated with observable quantities in probabilistic models. 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 `stochastic evidence' as well as revisit two older methods: Jeffrey's rule and virtual evidence. We devise concrete 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 we compare inference results associated with each interpretation.
翻译:我们考虑了在概率模型中进行贝叶斯推论的问题,在概率模型中,观测结果附有不确定性,称为“不确定证据”。在许多现实世界情景中,这种不确定性源于与概率模型中可观察到的数量有关的测量错误。我们探讨如何解释不确定证据,并进而探讨适当解释与潜在变量推论有关的重要性。我们考虑最近提出的一种“随机证据”方法,并重新审议两种老方法:Jeffrey的规则和虚拟证据。我们就如何计算不确定证据制定具体的指导方针,我们提供新的洞察力,特别是关于一致性的洞察力。为了展示对同一不确定证据的不同解释的影响,我们进行了实验,比较与每一种解释有关的推断结果。