Learning a tree-structured Ising model in order to make predictions
DOI10.1214/19-AOS1808zbMath1469.62334arXiv1604.06749MaRDI QIDQ2196190
Publication date: 28 August 2020
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1604.06749
predictionmodel selectionIsing modelMarkov random fieldshigh-dimensional statisticstree modelChow-Liu treemaximum likelihood tree
Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and prediction (62M20) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) (L^p)-limit theorems (60F25)
Related Items (6)
Cites Work
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- Can local particle filters beat the curse of dimensionality?
- Learning loopy graphical models with latent variables: efficient methods and guarantees
- Gibbs measures and phase transitions.
- High-dimensional structure estimation in Ising models: local separation criterion
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- Learning low-level vision
- A few logs suffice to build (almost) all trees. II
- Structure estimation for discrete graphical models: generalized covariance matrices and their inverses
- High-dimensional graphs and variable selection with the Lasso
- 10.1162/153244301753344605
- Efficiently Learning Ising Models on Arbitrary Graphs
- Phylogenies without Branch Bounds: Contracting the Short, Pruning the Deep
- Image denoising using scale mixtures of gaussians in the wavelet domain
- Graphical Models, Exponential Families, and Variational Inference
- On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)
- Information-Theoretic Limits of Selecting Binary Graphical Models in High Dimensions
- A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures
- Probability Inequalities for Sums of Bounded Random Variables
- Approximating discrete probability distributions with dependence trees
- Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms
- Introduction to nonparametric estimation
- Random cascades on wavelet trees and their use in analyzing and modeling natural images
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