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New algorithms tackle Gaussian graphical model selection from dependent data

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing new algorithms for a statistical modeling problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithms tackle Gaussian graphical model selection from dependent data

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The cluster contains an academic paper detailing new algorithms for a statistical modeling problem. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Vignesh Tirukkonda, Anirudh Rayas, Gautam Dasarathy ·

    Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics

    arXiv:2412.18594v3 Announce Type: replace-cross Abstract: 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 trajector…

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