Gaussian graphical models are parametric statistical models for jointly normal random variables whose dependence structure is determined by a graph. In previous work, we introduced trek separation, which gives a necessary and sufficient condition in terms of the graph for when a subdeterminant is zero for all covariance matrices that belong to the Gaussian graphical model. Here we extend this result to give explicit cancellation-free formulas for the expansions of nonzero subdeterminants.

Original language | English |
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Publisher | s.n. |
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Number of pages | 16 |
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Publication status | Published - 2012 |
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Name | arXiv.org |
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Volume | 1210.0390 [math.CO] |
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