Property testing in high-dimensional Ising models
From MaRDI portal
Publication:2328049
DOI10.1214/18-AOS1754zbMath1432.62039arXiv1709.06688MaRDI QIDQ2328049
Publication date: 9 October 2019
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1709.06688
Parametric hypothesis testing (62F03) Hypothesis testing in multivariate analysis (62H15) Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics (82B20)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Detection of correlations
- On combinatorial testing problems
- High-dimensional structure estimation in Ising models: local separation criterion
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- Combinatorial inference for graphical models
- Detecting Markov random fields hidden in white noise
- Global testing against sparse alternatives under Ising models
- Sparse permutation invariant covariance estimation
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Concentration inequalities for polynomials of contracting Ising models
- Exact recovery in the Ising blockmodel
- Property testing in high-dimensional Ising models
- Detecting positive correlations in a multivariate sample
- Inference in Ising models
- High-dimensional graphs and variable selection with the Lasso
- Efficiently Learning Ising Models on Arbitrary Graphs
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Model selection and estimation in the Gaussian graphical model
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Probability on Graphs
- Testing Ising Models
- Beitrag zur Theorie des Ferromagnetismus
- Information-Theoretic Limits of Selecting Binary Graphical Models in High Dimensions
- Curie–Weiss magnet—a simple model of phase transition
- Approximating discrete probability distributions with dependence trees
- Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms