摘要翻译：
代数几何与隐变量贝叶斯网络的推理框架之间的关系已经被许多作者进行了卓有成效的探索和开发。最近，因果贝叶斯网络的代数公式也在此背景下进行了研究。在回顾了这些新的关系之后，我们继续证明，因果模型概念中所包含的许多思想可以更普遍地直接用偏序和多项式映射族来表示。更传统的图形结构，如果可用，仍然是一个强大的工具。
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英文标题：
《Algebraic causality: Bayes nets and beyond》
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作者：
Eva Riccomagno and Jim Q Smith
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  The relationship between algebraic geometry and the inferential framework of the Bayesian Networks with hidden variables has now been fruitfully explored and exploited by a number of authors. More recently the algebraic formulation of Causal Bayesian Networks has also been investigated in this context. After reviewing these newer relationships, we proceed to demonstrate that many of the ideas embodied in the concept of a ``causal model'' can be more generally expressed directly in terms of a partial order and a family of polynomial maps. The more conventional graphical constructions, when available, remain a powerful tool.
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PDF链接：
https://arxiv.org/pdf/709.3377