Positivity for Gaussian graphical models

J. Draisma, S. Sullivant, K. Talaska

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
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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 non-zero subdeterminants.
Original languageEnglish
Pages (from-to)661-674
JournalAdvances in Applied Mathematics
Issue number5
Publication statusPublished - 2013


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