Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6419675 | Advances in Applied Mathematics | 2013 | 14 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jan Draisma, Seth Sullivant, Kelli Talaska,