Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have become increasingly vital in representing complex stochastic processes in various domains such as gene expression analysis, healthcare, and traffic prediction. Structure learning of DBNs from data is challenging, particularly for datasets with thousands of variables. Most current algorithms for DBN structure learning are adaptations from those used in static Bayesian Networks (BNs), and are typically focused on small-scale problems. In order to solve large-scale problems while taking full advantage of existing algorithms, this paper introduces a novel divide-and-conquer strategy, originally developed for static BNs, and adapts it for large-scale DBN structure learning. In this work, we specifically concentrate on 2 Time-sliced Bayesian Networks (2-TBNs), a special class of DBNs. Furthermore, we leverage the prior knowledge of 2-TBNs to enhance the performance of the strategy we introduce. Our approach significantly improves the scalability and accuracy of 2-TBN structure learning. Experimental results demonstrate the effectiveness of our method, showing substantial improvements over existing algorithms in both computational efficiency and structure learning accuracy. On problem instances with more than 1,000 variables, our approach improves two accuracy metrics by 74.45% and 110.94% on average , respectively, while reducing runtime by 93.65% on average.

翻译：动态贝叶斯网络（DBNs）以其可解释性著称，在基因表达分析、医疗健康和交通预测等领域的复杂随机过程建模中日益重要。从数据中学习DBN结构极具挑战性，尤其是处理包含数千个变量的数据集时。当前大多数DBN结构学习算法均改编自静态贝叶斯网络（BNs）算法，且主要针对小规模问题。为充分利用现有算法解决大规模问题，本文引入一种最初为静态BNs设计的新型分治策略，并将其适配用于大规模DBN结构学习。本研究重点关注DBNs的特殊子类——2-时间片贝叶斯网络（2-TBNs）。此外，我们利用2-TBNs的先验知识来增强所引入策略的性能。该方法显著提升了2-TBN结构学习的可扩展性与准确率。实验结果表明，我们的方法在计算效率和结构学习准确率上均较现有算法有显著改进。在包含超过1000个变量的实例中，本方法使两项准确率指标平均分别提升74.45%和110.94%，同时将运行时间平均降低93.65%。