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 implementation of a 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. [23]. 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.

动态贝叶斯网络在文献中作为离散时间模型得到了很好的探索：然而，它们的连续时间扩展很少受到关注。在本文中，我们提出了一种基于约束的算法的第一个实现，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和我们的提议用于建立条件独立性的基本假设。此外，我们分析和讨论了所提出算法的最佳和最坏情况的计算复杂度。最后，我们使用合成数据验证其性能，并将其与 Nodelman 等人的基于分数的结构学习算法进行比较，讨论其优势和局限性。[23]。我们发现后者在学习具有二元变量的网络时更准确，而我们基于约束的方法对于假设两个以上值的变量更准确。数值实验证实，基于分数和基于约束的算法在计算时间方面具有可比性。