This study presents a dynamic Bayesian network framework that facilitates intuitive gradual edge changes. We use two conditional dynamics to model the edge addition and deletion, and edge selection separately. Unlike previous research that uses a mixture network approach, which restricts the number of possible edge changes, or structural priors to induce gradual changes, which can lead to unclear network evolution, our model induces more frequent and intuitive edge change dynamics. We employ Markov chain Monte Carlo (MCMC) sampling to estimate the model structures and parameters and demonstrate the model's effectiveness in a portfolio selection application.

翻译：本研究提出了一种动态贝叶斯网络框架，该框架支持直观的渐进式边变化。我们分别使用两种条件动态来建模边的添加与删除以及边的选择。与先前研究不同——先前研究或采用混合网络方法（该方法限制了可能的边变化数量），或采用结构先验来诱导渐进变化（这可能导致网络演化过程不清晰），我们的模型能够诱导出更频繁且更直观的边变化动态。我们采用马尔可夫链蒙特卡洛（MCMC）采样方法来估计模型结构与参数，并在一个投资组合选择应用中验证了模型的有效性。