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New algorithm learns Gaussian graphical models from single trajectory

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new polynomial-time algorithm capable of recovering the conditional-independence graph of a Gaussian graphical model from a single trajectory of Glauber dynamics. This method does not require the trajectory to reach its mixing time, addressing a gap in current capabilities for temporally correlated observations. The algorithm involves estimating conditional variances, rescaling the trajectory, and employing a local edge test with a robust median-based estimator to ensure accuracy despite temporal dependencies. AI

IMPACT This research advances methods for analyzing complex, temporally correlated data, potentially impacting fields that rely on graphical models for understanding relationships.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for learning Gaussian graphical models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithm learns Gaussian graphical models from single trajectory

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Eric Shen, Tony Wu, Mahbod Majid, Ankur Moitra ·

    Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing

    arXiv:2606.31230v1 Announce Type: new Abstract: We study the task of learning the structure of a $d$-sparse Gaussian graphical model on $n$ variables from a single trajectory of Glauber dynamics. Beyond algorithmic considerations, many applications present temporally correlated o…

  2. arXiv stat.ML TIER_1 English(EN) · Ankur Moitra ·

    Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing

    We study the task of learning the structure of a $d$-sparse Gaussian graphical model on $n$ variables from a single trajectory of Glauber dynamics. Beyond algorithmic considerations, many applications present temporally correlated observations rather than i.i.d.\ samples. In the …

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