Learning a tree-structured Ising model in order to make predictions

DOI10.1214/19-AOS1808zbMath1469.62334arXiv1604.06749MaRDI QIDQ2196190

Guy Bresler, Mina Karzand

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

zbMATH Keywords

predictionmodel selectionIsing modelMarkov random fieldshigh-dimensional statisticstree modelChow-Liu treemaximum likelihood tree

Mathematics Subject Classification ID

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)

Generative modeling via tree tensor network states ⋮ Identifiability in robust estimation of tree structured models ⋮ Near-Optimal Learning of Tree-Structured Distributions by Chow and Liu ⋮ Unnamed Item ⋮ Bayesian model selection for high-dimensional Ising models, with applications to educational data ⋮ Stein's method for stationary distributions of Markov chains and application to Ising models

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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