Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian networks, these models can encode context-specific conditional independencies as well as asymmetric developments within the evolution of a process. More recently, new model classes belonging to the chain event graph family have been developed for modelling time-to-event data to study the temporal dynamics of a process. However, existing model selection algorithms for chain event graphs and its variants rely on all parameters having conjugate priors. This is unrealistic for many real-world applications. In this paper, we propose a mixture modelling approach to model selection in chain event graphs that does not rely on conjugacy. Moreover, we also show that this methodology is more amenable to being robustly scaled than the existing model selection algorithms used for this family. We demonstrate our techniques on simulated datasets.
翻译:链条事件图形是一系列概率化图形模型的组合,这些模型概括了贝叶西亚网络,并成功地应用于广泛的领域。与巴伊西亚网络不同,这些模型可以将特定环境的有条件依赖性以及一个过程演变过程中的不对称发展动态编码成一个系统。最近,已经开发了属于链条事件图表系列的新模型类别,用于模拟时间-活动数据,以研究一个过程的时间-活动动态。然而,现有的链条事件图表及其变量的模型选择算法依赖具有共性前科的所有参数。这对许多真实世界应用来说是不现实的。在本文中,我们建议对不依赖共性的一系列事件图表的模型选择采用混合建模方法。此外,我们还表明,这一方法比目前用于这个家庭使用的模型选择算法更容易被强有力地缩放。我们在模拟数据集上展示了我们的技术。