2012/10/01 by Jan Draisma, Draisma, Jan, Seth Sullivant +3 · 1 citation
Computer Science · Mathematics · #Advanced Database Systems and Queries #Bayesian Modeling and Causal Inference #Combinatorics (math.CO) #Data Management and Algorithms #FOS: Mathematics #Statistics Theory (math.ST) #math.CO #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1210.0390
16 pages, 3 figures
arxiv created 2012/10/01 · openalex publication_date 2012/10/01 · arxiv updated 2012/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.