用可计算概率电路加速因果结构学习中的边缘化。
How to Marginalize in Causal Structure Learning?
- 用可计算概率电路替代传统动态规划做边缘化。
- 在真实数据上提升因果结构推断准确率。
- 适合需要高效边缘化的因果推理研究者。
贝叶斯网络（BNs）是广泛应用的概率图模型，但基于数据推断其图结构仍具挑战。贝叶斯结构学习通过推断可能的有向无环图后验分布来解决此问题，常需对概率分布进行边缘化，传统方法依赖动态规划并限制每个节点的父节点集合。本文提出一种新方法，利用可计算概率电路绕过此限制。该方法通过同时训练电路拟合原始分布和边缘查询，使电路架构天然支持快速精确的边缘化。实验表明，使用该方法处理边缘化可显著提升贝叶斯结构学习的性能，优于现有方法。
原文摘要 · Abstract (English)
Bayesian networks (BNs) are a widely used class of probabilistic graphical models employed in numerous application domains. However, inferring the network's graphical structure from data remains challenging. Bayesian structure learners approach this problem by inferring a posterior distribution over the possible directed acyclic graphs underlying the BN. The inference process often requires marginalizing over probability distributions, which is typically done using dynamic programming methods that restrict the set of possible parents for each node. Instead, we present a novel method that utilizes tractable probabilistic circuits to circumvent this restriction. This method utilizes a new learning routine that trains these circuits on both the original distribution and marginal queries. The architecture of probabilistic circuits then inherently allows for fast and exact marginalization on the learned distribution. We then show empirically that utilizing our method to answer marginals allows Bayesian structure learners to improve their performance compared to current methods.
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