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stat.ML2026
Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics
Vignesh Tirukkonda, Gautam Dasarathy
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.…
stat.ML2024
Learning Networks from Wide-Sense Stationary Stochastic Processes
Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri +2
Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from…