Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics
Researchers have developed new algorithms for Gaussian graphical model selection when data comes from dependent dynamics, rather than independent samples. One approach uses a local edge-testing estimator that can be implemented in parallel and does not require the data chain to fully mix. The second method involves a burn-in and thinning reduction, proving that a subsampled trajectory can approximate independent samples, allowing standard learners to be used. Both methods include finite-sample recovery guarantees and information-theoretic lower bounds on observation time. AI
IMPACT Introduces novel algorithmic approaches for statistical inference in dependent data settings, potentially improving model selection accuracy in complex systems.