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Will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software.
Fundamental Conditional Independencies for Nondecomposable Loglinear Models
Fundamental Conditional Independencies for Nondecomposable Loglinear Models
For nondecomposable loglinear models (LLMs), the construction of fundamental conditional independencies (FCIs) is more complex, partly because the multiset of branches of a maximum spanning tree need not be unique. So we will use a graph theory tool ...
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