In this paper, we present a guide to the foundations of learning Dynamic Bayesian Networks (DBNs) from data in the form of multiple samples of trajectories for some length of time. We present the formalism for a generic as well as a set of common types of DBNs for particular variable distributions. We present the analytical form of the models, with a comprehensive discussion on the interdependence between structure and weights in a DBN model and their implications for learning. Next, we give a broad overview of learning methods and describe and categorize them based on the most important statistical features, and how they treat the interplay between learning structure and weights. We give the analytical form of the likelihood and Bayesian score functions, emphasizing the distinction from the static case. We discuss functions used in optimization to enforce structural requirements. We briefly discuss more complex extensions and representations. Finally we present a set of comparisons in different settings for various distinct but representative algorithms across the variants.

翻译：本文为从数据中学习动态贝叶斯网络（DBN）提供了基础性指南，数据形式为多个样本在特定时间长度内的轨迹序列。我们首先给出通用DBN的形式化表述，以及针对特定变量分布的常见DBN类型集合。通过展示模型的解析形式，深入探讨DBN模型中结构与权重之间的相互依赖关系及其对学习过程的影响。随后，系统综述各类学习方法，依据关键统计特征及其处理结构与权重交互作用的方式进行分类阐述。我们给出似然函数和贝叶斯评分函数的解析形式，重点阐明其与静态情况的区别。同时讨论优化过程中用于强化结构约束的函数设计，并简要概述更复杂的扩展模型与表示方法。最后，我们在不同设置下对各类变体中具有代表性的算法进行了多组对比实验。