In this work, we demonstrate the Empirical Bayes approach to learning a Dynamic Bayesian Network. By starting with several point estimates of structure and weights, we can use a data-driven prior to subsequently obtain a model to quantify uncertainty. This approach uses a recent development of Generalized Variational Inference, and indicates the potential of sampling the uncertainty of a mixture of DAG structures as well as a parameter posterior.

翻译：本研究展示了利用经验贝叶斯方法学习动态贝叶斯网络的框架。通过从若干结构和权重的点估计出发，我们能够采用数据驱动的先验分布来获得量化不确定性的模型。该方法运用了广义变分推断的最新进展，并揭示了同时对有向无环图结构混合分布与参数后验分布进行不确定性采样的潜力。