High-dimensional structure learning of binary pairwise Markov networks: a comparative numerical study
type: Item
instance of: scholarly article
title: High-dimensional structure learning of binary pairwise Markov networks: a comparative numerical study
author: Johan Pensar
author: Santeri Puranen
author: Maiju Pesonen
author: Yoshiyuki Kabashima
author: Jukka Corander
author: Yingying Xu
published in: Computational Statistics and Data Analysis
publication date: 2019-11-22T00:00:00Z
full work available at URL: https://arxiv.org/abs/1901.04345
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 7135456
zbMATH Keywords: Markov network
zbMATH Keywords: Ising model
zbMATH Keywords: structure learning
zbMATH Keywords: mutual information
zbMATH Keywords: pseudo-likelihood
zbMATH Keywords: Gibbs sampler
describes a project that uses: LIBLINEAR
describes a project that uses: CNM
describes a project that uses: L1General
describes a project that uses: ARACNE
MaRDI profile type: Publication
OpenAlex ID: W2962845351
cites work: Emergence of Scaling in Random Networks
cites work: High-dimensional Ising model selection with Bayesian information criteria
cites work: Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
cites work: Q3096176
cites work: Ridge Regression: Biased Estimation for Nonorthogonal Problems
cites work: Estimation of sparse binary pairwise Markov networks using pseudo-likelihoods
cites work: Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines
cites work: Q3655273
cites work: High-dimensional graphs and variable selection with the Lasso
cites work: High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
cites work: Collective dynamics of ‘small-world’ networks
cites work: Q3358073
DOI: 10.1016/J.CSDA.2019.06.012
description: scientific article; zbMATH DE number 7135456