提升贝叶斯网络结构采样效率，加速复杂图结构推理。
Scaling Up Bayesian DAG Sampling
- 改进基本操作的实现，高效执行弧的增删改
- 通过预处理剪枝父节点集合，近似保留求和结果
- 适合需要快速采样贝叶斯网络结构的研究者
贝叶斯网络结构的贝叶斯推断通常通过在构造好的马尔可夫链上采样有向无环图（DAG）来实现。本文提出两种改进方法：首先，优化了基础操作的实现，包括添加、删除或反转单条弧；其次，加速了对父节点集合的求和过程，该过程在更复杂的移动中计算成本高昂——我们设计了一种预处理方法，通过剪枝可能的父节点集合，在近似保持求和结果的前提下显著降低计算量。实验表明，相比以往方法，本方法可带来显著的效率提升。
原文摘要 · Abstract (English)
Bayesian inference of Bayesian network structures is often performed by sampling directed acyclic graphs along an appropriately constructed Markov chain. We present two techniques to improve sampling. First, we give an efficient implementation of basic moves, which add, delete, or reverse a single arc. Second, we expedite summing over parent sets, an expensive task required for more sophisticated moves: we devise a preprocessing method to prune possible parent sets so as to approximately preserve the sums. Our empirical study shows that our techniques can yield substantial efficiency gains compared to previous methods.
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