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This chapter provides the requisite foundations for the remaining chapters in Part II and for some of the results established in Part III. We use graphs to represent conditional independence among counter and label variables. This leads to a probabilistic graphical model, defined via a graph in which the vertex set is the set of counter and label variables, and the edge set effectively determines the degree of conditional independence among these variables.

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Conditional Independence

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Antonius B. Dieker,
  • Steven T. Hackman

摘要

This chapter provides the requisite foundations for the remaining chapters in Part II and for some of the results established in Part III. We use graphs to represent conditional independence among counter and label variables. This leads to a probabilistic graphical model, defined via a graph in which the vertex set is the set of counter and label variables, and the edge set effectively determines the degree of conditional independence among these variables.