论文标题
结构学习的不确定性
Tractable Uncertainty for Structure Learning
论文作者
论文摘要
贝叶斯结构学习使人们可以捕获负责生成给定数据的因果定向无环图（DAG）的不确定性。在这项工作中，我们提出了结构学习（Trust）的可疗法不确定性，这是近似后推断的框架，依赖于概率回路作为我们后验信仰的表示。与基于样本的后近似值相反，我们的表示形式可以捕获一个更丰富的DAG空间，同时也能够通过一系列有用的推理查询来谨慎地理解不确定性。我们从经验上展示了如何将概率电路用作结构学习方法的增强表示，从而改善了推断的结构和后不确定性的质量。有条件查询的实验结果进一步证明了信任的表示能力的实际实用性。
Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilistic circuits as the representation of our posterior belief. In contrast to sample-based posterior approximations, our representation can capture a much richer space of DAGs, while also being able to tractably reason about the uncertainty through a range of useful inference queries. We empirically show how probabilistic circuits can be used as an augmented representation for structure learning methods, leading to improvement in both the quality of inferred structures and posterior uncertainty. Experimental results on conditional query answering further demonstrate the practical utility of the representational capacity of TRUST.
