A global approach for learning sparse Ising models
type: Item
instance of: scholarly article
title: A global approach for learning sparse Ising models
published in: Mathematics and Computers in Simulation
publication date: 2021-03-06T00:00:00Z
full work available at URL: https://arxiv.org/abs/1906.11641
Mathematics Subject Classification ID: 62Hxx
Mathematics Subject Classification ID: 05Cxx
zbMATH DE Number: 7318068
zbMATH Keywords: Ising models
zbMATH Keywords: pairwise Markov graphs
zbMATH Keywords: \( l_1\) penalty
zbMATH Keywords: logistic regression
author: Daniela De Canditiis
describes a project that uses: glasso
MaRDI profile type: Publication
OpenAlex ID: W2955752281
cites work: High-dimensional structure estimation in Ising models: local separation criterion
cites work: Efficiently Learning Ising Models on Arbitrary Graphs
cites work: Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms
cites work: Approximating discrete probability distributions with dependence trees
cites work: Estimation of sparse binary pairwise Markov networks using pseudo-likelihoods
cites work: Q3655273
cites work: Q4382293
cites work: High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
DOI: 10.1016/J.MATCOM.2020.02.012
description: scientific article; zbMATH DE number 7318068