Gaussian Graphical Model
PulseAugur coverage of Gaussian Graphical Model — every cluster mentioning Gaussian Graphical Model across labs, papers, and developer communities, ranked by signal.
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New algorithm learns Gaussian graphical models from single trajectory
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 requir…
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New spectral sparsification methods enhance graphical model accuracy
Researchers have developed new methods, Spectral-LCGGM and Spectral-HR, to improve the accuracy and scalability of Laplacian-constrained Gaussian and Hüsler-Reiss graphical models. These models are used in areas like gr…
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New algorithms tackle Gaussian graphical model selection from dependent data
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 imp…
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New proximal projection method improves doubly sparse regularized models
Researchers have developed a novel proximal projection method for doubly sparse regularized models in high-dimensional regression settings. This approach leverages the structure of Gaussian graphical models to decompose…