Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
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 dynamics. Existing techniques for this problem either inherit the mixing time of the chain, which can be super-polynomial in the dimension $p$ without strong assumptions, or are suboptimal in the minimum normalized edge strength $κ$. We propose two algorithms that are mixing-free and attain the $κ^{-2}$ dependence of the information-theoretic lower bounds. Both instantiate a shared dueling-neighborhood search meta-algorithm ...
| Authors | Vignesh Tirukkonda, Gautam Dasarathy |
|---|---|
| Primary category | stat.ML |
| Published | 20 July 2026 |
| Last updated at source | 4 August 2026 |
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