论文标题
动态连续时链事件图的传播图
Propagation for Dynamic Continuous Time Chain Event Graphs
论文作者
论文摘要
连锁事件图（CEGS）是一个基于事件的图形模型家族，代表通常由不对称状态空间问题表现出的上下文特定条件独立性。连续时间动态CEGS（CT-DCEG）类别提供了连续时间过程中过程的纵向发展轨迹的分类表示。 CT-DCEG中的时间证据引入了其过渡和持有时间分布之间的依赖性。我们为离散的动态贝叶斯网络（DBN）提供了类似于Kjærulff（1992）中该方案的可拖动的推理方案，该方案通过“展开” DBN采用标准的连接树推断。为了启用该方案，我们提出了标准CEG传播算法的扩展（Thwaites等，2008）。有趣的是，CT-DCEG在观察兼容证据上的简化图中受益，同时保留了非对称网络中仍然相关的对称性。我们的结果表明，在涉及显着不对称性和过程演化的自然总排序的情况下，CT-DCEG优先于DBN和连续的时间BN。
Chain Event Graphs (CEGs) are a family of event-based graphical models that represent context-specific conditional independences typically exhibited by asymmetric state space problems. The class of continuous time dynamic CEGs (CT-DCEGs) provides a factored representation of longitudinally evolving trajectories of a process in continuous time. Temporal evidence in a CT-DCEG introduces dependence between its transition and holding time distributions. We present a tractable exact inferential scheme analogous to the scheme in Kjærulff (1992) for discrete Dynamic Bayesian Networks (DBNs) which employs standard junction tree inference by "unrolling" the DBN. To enable this scheme, we present an extension of the standard CEG propagation algorithm (Thwaites et al., 2008). Interestingly, the CT-DCEG benefits from simplification of its graph on observing compatible evidence while preserving the still relevant symmetries within the asymmetric network. Our results indicate that the CT-DCEG is preferred to DBNs and continuous time BNs under contexts involving significant asymmetry and a natural total ordering of the process evolution.
