Staged tree models are a discrete generalization of Bayesian networks. We show that these form curved exponential families and derive their natural parameters, sufficient statistic, and cumulant-generating function as functions of their graphical representation. We give necessary and sufficient graphical criteria for classifying regular subfamilies and discuss implications for model selection.
翻译:分阶段树型模型是贝叶斯网络的离散概括化。 我们显示这些模型形成曲线指数式家庭,并得出其自然参数、足够的统计数据和累积生成功能,作为其图形表达功能。 我们给出了必要和充分的图形标准,用于对普通次家庭进行分类,并讨论模型选择的影响。