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.
翻译:贝叶斯结构的学习使人们能够捕捉到对生成特定数据负责的因果定向圆形图(DAG)的不确定性。在这项工作中,我们提出了结构学习的可转移不确定性(TRust ), 这是一种近似事后推论的框架,它以概率性电路作为我们后方信仰的表现形式。 与基于样本的后近似近似相比,我们的代表性可以捕捉到更丰富的数据包空间,同时能够通过一系列有用的推论查询对不确定性进行清晰的推理。 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强化代表,从而导致推断结构质量的提高和后方不确定性的提高。 有条件回答的实验结果进一步证明了TRust的代表性能力的实际效用。