论文标题
一种基于约束的算法，用于连续时间贝叶斯网络的结构学习
A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks
论文作者
论文摘要
在文献中，动态贝叶斯网络作为离散时间模型进行了很好的探索：但是，它们的连续时间扩展的关注相对较少。在本文中，我们提出了第一个基于约束的算法，用于学习连续时间贝叶斯网络的结构。我们讨论了不同的统计检验以及我们的提议建立条件独立性的潜在假设。此外，我们分析并讨论了所提出算法的最佳和最坏情况的计算复杂性。最后，我们使用合成数据验证其性能，并讨论了它的优势和局限性，将其与Nodelman等人的基于得分的结构学习算法进行了比较。 （2003）。我们发现后者在具有二进制变量的学习网络中更准确，而我们基于约束的方法更准确，而变量假设超过两个值。数值实验证实，基于得分和基于约束的算法在计算时间方面是可比的。
Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In this paper, we propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence. Furthermore, we analyze and discuss the computational complexity of the best and worst cases for the proposed algorithm. Finally, we validate its performance using synthetic data, and we discuss its strengths and limitations comparing it with the score-based structure learning algorithm from Nodelman et al. (2003). We find the latter to be more accurate in learning networks with binary variables, while our constraint-based approach is more accurate with variables assuming more than two values. Numerical experiments confirm that score-based and constraint-based algorithms are comparable in terms of computation time.
