变结构动态贝叶斯网络的机制研究

传统的动态贝叶斯网络(Dynamic Bayesian networks, DBNs)描述的是一个稳态过程,而处理非稳态过程,变结构动态贝叶斯网络更适 用、更灵活、更有效.为了克服现有变结构离散 动态贝叶斯网络推理算法只能处理硬证据的缺陷,本文在深入分析变结构动态贝叶斯网络机制及其特 征的基础上,提出了变结构离散动态贝叶斯网络的 快速推理算法.此外,对变结构动态贝叶斯网络的特例,即数据缺失动态贝叶斯网络进行了定义并构建 了相应的模型.仿真实验验证了变结构离散动态贝 叶斯网络快速推理算法的有效性及计算效率.

Study on the Mechanism of Structure-variable Dynamic Bayesian Networks

Traditional dynamic Bayesian networks (DBNs) are essentially models that describe a variety of stable processes. To deal with unstable processes, structure-variable dynamic Bayesian networks are more applicable, flexible, and effective. Currently, however, the various inference algorithms under consideration for structure-variable discrete dynamic Bayesian networks (DDBNs) can only handle hard evidence. In this paper, an in-depth and theoretical analysis is given for the mechanism and key characteristics of structure-variable dynamic Bayesian networks, and on this basis, a fast inference algorithm is proposed. Furthermore, a special class of structure-variable dynamic Bayesian networks, dynamic Bayesian networks with missing data, is defined rigorously along with associated network topology and parameter settings of such networks. Several experimental simulations have shown the effectiveness and efficiency of our fast inference algorithm.