动态连续时间链事件图的传播
Propagation for Dynamic Continuous Time Chain Event Graphs
摘要：
链事件图（Chain Event Graph，CEG）是一类基于事件的图形模型，它代表了非对称状态空间问题中典型的上下文特定条件独立性。连续时间动态CEG（CT-DCEG）类提供了一个连续时间的过程的纵向演变轨迹的因子表示。CT-DCEG中的时间证据在其过渡和保持时间分布之间引入了依赖性。我们提出了一个易于处理的精确推理方案类似于Kjærulff（1992）中的离散动态贝叶斯网络（DBN）的方案，该方案通过“展开”DBN来使用标准的连接树推理。为了实现该方案，我们提出了标准CEG传播算法的扩展（Thwaites等人，2008年）。有趣的是，CT-DCEG在观察兼容证据时受益于其图的简化，同时保留了非对称网络中仍然相关的对称性。我们的研究结果表明，CT-DCEG是首选的DBN和连续时间BN的背景下，涉及显着的不对称性和过程演化的自然全序。
英文摘要：
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.
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