AI RESEARCH
Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics
arXiv CS.LG
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ArXi:2412.18594v3 Announce Type: replace Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamics. We study structure learning when the data consist of a single trajectory of Gaussian Glauber dynamics. We develop two complementary approaches. The first is a local edge-testing estimator based on an appropriately designed correlation test that reveals edges. This estimator does not require waiting for the chain to mix and admits an embarrassingly parallel edgewise implementation.