离散可分解模型的一个新刻画
A new characterization of discrete decomposable models
摘要：
可分解图模型，也称为完美DAG模型，在现代机器学习和统计学中通过图表示进行概率推理的标准方法中发挥着重要作用。然而，这样的模型是有限的假设，数据生成分布不需要严格的上下文特定的条件独立关系。阶段树模型的家庭一般化DAG模型，以适应特定于上下文的知识。我们提供了一个新的表征完美的离散DAG模型的阶段树表示。这种特性确定家庭的平衡分期树的离散可分解模型的上下文特定的设置的自然推广。
英文摘要：
Decomposable graphical models, also known as perfect DAG models, play a fundamental role in standard approaches to probabilistic inference via graph representations in modern machine learning and statistics. However, such models are limited by the assumption that the data-generating distribution does not entail strictly context-specific conditional independence relations. The family of staged tree models generalizes DAG models so as to accommodate context-specific knowledge. We provide a new characterization of perfect discrete DAG models in terms of their staged tree representations. This characterization identifies the family of balanced staged trees as the natural generalization of discrete decomposable models to the context-specific setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
