Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesian Network graphs from synthetic data. However, in their mission to maximise a scoring function, many become conservative and minimise edges discovered. While simplicity is desired, the output is often a graph that consists of multiple independent subgraphs that do not enable full propagation of evidence. While this is not a problem in theory, it can be a problem in practice. This article examines a novel unconventional associational heuristic called Saiyan, which returns a directed acyclic graph that enables full propagation of evidence. Associational heuristics are not expected to perform well relative to sophisticated constraint-based and score-based learning approaches. Moreover, forcing the algorithm to connect all data variables implies that the forced edges will not be correct at the rate of those identified unrestrictedly. Still, synthetic and real-world experiments suggest that such a heuristic can be competitive relative to some of the well-established constraint-based, score-based and hybrid learning algorithms.

中文翻译：

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使用 Saiyan 算法学习贝叶斯网络

一些结构学习算法已被证明可以有效地从合成数据中重建假设的贝叶斯网络图。然而，在最大化评分函数的任务中，许多人变得保守并最小化发现的边缘。虽然需要简单性，但输出通常是由多个独立子图组成的图，这些子图无法充分传播证据。虽然这在理论上不是问题，但在实践中可能是一个问题。本文研究了一种名为 Saiyan 的新型非传统关联启发式算法，它返回一个有向无环图，可以实现证据的完全传播。相对于复杂的基于约束和基于分数的学习方法，预计关联启发式不会表现良好。而且，强制算法连接所有数据变量意味着强制边缘不会以不受限制地识别的速率正确。尽管如此，合成和现实世界的实验表明，相对于一些成熟的基于约束、基于分数和混合学习算法，这种启发式算法可以具有竞争力。