Positivity for Gaussian graphical models

J. Draisma, S. Sullivant, K. Talaska

Research output: Book/ReportReportAcademic


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 languageEnglish
Number of pages16
Publication statusPublished - 2012

Publication series

Volume1210.0390 [math.CO]


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