A global approach for learning sparse Ising models

DOI10.1016/j.matcom.2020.02.012OpenAlexW2955752281MaRDI QIDQ1998053

Daniela de Canditiis

Publication date: 6 March 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.11641

zbMATH Keywords

Ising modelslogistic regression\( l_1\) penaltypairwise Markov graphs

Mathematics Subject Classification ID

Graph theory (05Cxx) Multivariate analysis (62Hxx)

Related Items (2)

Learning binary undirected graph in low dimensional regime ⋮ Applied scientific computing XV: innovative modeling and simulation in sciences

Uses Software

• glasso

Cites Work

• High-dimensional structure estimation in Ising models: local separation criterion
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Efficiently Learning Ising Models on Arbitrary Graphs
• Approximating discrete probability distributions with dependence trees
• Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms
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