论文标题
超越连接链事件图形模型选择
Beyond Conjugacy for Chain Event Graph Model Selection
论文作者
论文摘要
连锁事件图是一个概率图形模型的家族，它们概括了贝叶斯网络，并已成功应用于广泛的域。与贝叶斯网络不同，这些模型可以在过程演变中编码上下文特定的条件独立性以及不对称的发展。最近，已经开发了属于链事件图的新模型类，用于建模事件时间数据以研究过程的时间动态。但是，链事件图及其变体的现有模型选择算法依赖于具有共轭先验的所有参数。对于许多现实世界应用来说，这是不现实的。在本文中，我们提出了一种混合建模方法，以在不依赖共轭的链事件图中进行模型选择。此外，我们还表明，与该家族使用的现有模型选择算法相比，这种方法更适合于稳健地缩放。我们在模拟数据集上演示了我们的技术。
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.
