Polynomial-time algorithm recovers the conditional-independence graph of a d-sparse GGM from one Glauber trajectory with length independent of mixing time.
Annals of Statistics , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A new statistical method integrates database priors into conditional GGMs via a structured weighted penalty for improved population-level and context-specific PPI network reconstruction, validated in simulations and UK Biobank cardiometabolic proteomics data.
New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.
citing papers explorer
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Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing
Polynomial-time algorithm recovers the conditional-independence graph of a d-sparse GGM from one Glauber trajectory with length independent of mixing time.
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Prior-informed conditional Gaussian graphical models: an application to protein interaction network reconstruction
A new statistical method integrates database priors into conditional GGMs via a structured weighted penalty for improved population-level and context-specific PPI network reconstruction, validated in simulations and UK Biobank cardiometabolic proteomics data.
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Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
New MCMC methods employ data-driven similarity-driven proposals to improve sampling from posteriors on discrete state spaces, extending to hierarchical models without marginalizing latent variables.