arXiv stat.ML
· Papers
Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
arXiv:2607.18559v1 Announce Type: new Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process. We study exact recovery of the graph from one trajectory of random-scan Gaussian Glauber dyn