Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.aam.2013.03.001 | Advances in Applied Mathematics |
Keywords | Field | DocType |
sufficient condition,parametric statistical model,gaussian graphical model,covariance matrix,normal random variable,non-zero subdeterminants,explicit cancellation-free formula,dependence structure,previous work,trek separation,determinant,conditional independence | Random variable,Combinatorics,Gaussian random field,Matrix (mathematics),Mathematical analysis,Parametric statistics,Gaussian,Statistical model,Graphical model,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
50 | 5 | 0196-8858 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Draisma | 1 | 20 | 5.82 |
| Seth Sullivant | 2 | 93 | 19.17 |
| Kelli Talaska | 3 | 1 | 0.99 |