论文标题
可变排序对贝叶斯网络结构学习的影响
The Impact of Variable Ordering on Bayesian Network Structure Learning
论文作者
论文摘要
因果贝叶斯网络为在不确定性下进行推理提供了重要的工具，并可能应用于许多复杂的因果系统。结构学习算法可以告诉我们有关这些系统因果结构的一些信息，这变得越来越重要。在文献中，这些算法的有效性通常经过对不同样本量，超参数以及偶尔客观函数的灵敏度进行测试。在本文中，我们表明，从数据中读取变量的顺序可能比这些因素对算法的准确性产生更大的影响。由于变量排序是任意的，因此它对学习的图表精度的任何重大影响都与之有关，这引发了有关算法对敏感但尚未针对不同可变订单敏感但尚未评估的结果的有效性的问题。
Causal Bayesian Networks provide an important tool for reasoning under uncertainty with potential application to many complex causal systems. Structure learning algorithms that can tell us something about the causal structure of these systems are becoming increasingly important. In the literature, the validity of these algorithms is often tested for sensitivity over varying sample sizes, hyper-parameters, and occasionally objective functions. In this paper, we show that the order in which the variables are read from data can have much greater impact on the accuracy of the algorithm than these factors. Because the variable ordering is arbitrary, any significant effect it has on learnt graph accuracy is concerning, and this raises questions about the validity of the results produced by algorithms that are sensitive to, but have not been assessed against, different variable orderings.
