论文标题
概率模型和随机模拟器中的不确定证据
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
论文作者
论文摘要
我们考虑在概率模型中进行贝叶斯推论的问题，在概率模型中，观察结果伴随着不确定性，称为“不确定证据”。我们探索如何解释不确定的证据，并通过扩展适当解释的重要性，因为它与对潜在变量的推断有关。我们考虑了一种最近提供的方法“分配证据”，并重新审视了两种较旧的方法：杰弗里的规则和虚拟证据。我们制定了有关如何解释不确定证据的准则，我们提供了新的见解，尤其是关于一致性。为了展示对相同不确定证据的不同解释的影响，我们进行了实验，其中一种解释被定义为“正确”。然后，我们比较了每种不同解释的推论结果，以说明仔细考虑不确定证据的重要性。
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.
