论文标题
贝叶斯模型的平均链事件图，用于强大的解释建模
Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling
论文作者
论文摘要
链事件图（CEGS）是一种广泛适用的概率图形模型类，可以以易于解释的方式表示事件的特定于上下文独立语句和不对称展开。有关CEG的现有模型选择文献主要集中在获得最大后验（MAP）CEG。但是，MAP选择众所周知，可以忽略模型不确定性。在这里，我们探讨了贝叶斯模型在此类中平均的使用。我们证明了这种方法如何通过识别多个高分模型的共享特征来量化模型不确定性并导致更健壮的推断。由于可能的CEG的空间是巨大的，因此除了小问题以外的所有问题外，对模型平均的模型进行了详尽的评分。但是，我们对现有模型选择算法进行了简单的修改，该算法可以采样模型空间，以说明与更标准的MAP建模相比，贝叶斯模型平均的功效。
Chain Event Graphs (CEGs) are a widely applicable class of probabilistic graphical model that can represent context-specific independence statements and asymmetric unfoldings of events in an easily interpretable way. Existing model selection literature on CEGs has largely focused on obtaining the maximum a posteriori (MAP) CEG. However, MAP selection is well-known to ignore model uncertainty. Here, we explore the use of Bayesian model averaging over this class. We demonstrate how this approach can quantify model uncertainty and leads to more robust inference by identifying shared features across multiple high-scoring models. Because the space of possible CEGs is huge, scoring models exhaustively for model averaging in all but small problems is prohibitive. However, we provide a simple modification of an existing model selection algorithm, that samples the model space, to illustrate the efficacy of Bayesian model averaging compared to more standard MAP modelling.
