An oracle approach for interaction neighborhood estimation in random fields

DOI10.1214/11-EJS618zbMath1274.62641arXiv1010.4783OpenAlexW2010952705WikidataQ98839737 ScholiaQ98839737MaRDI QIDQ1952198

Daniel Y. Takahashi, Matthieu Lerasle

Publication date: 28 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1010.4783

zbMATH Keywords

model selectionIsing modelcomputationally efficient algorithm

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (3)

Estimating the interaction graph of stochastic neural dynamics ⋮ Sharp oracle inequalities and slope heuristic for specification probabilities estimation in discrete random fields ⋮ Identifying interacting pairs of sites in Ising models on a countable set

Uses Software

• spatial
• CAPUSHE

Cites Work

• Unnamed Item
• Slope heuristics: overview and implementation
• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Gibbs measures and phase transitions
• A Bennett concentration inequality and its application to suprema of empirical processes
• Identifying interacting pairs of sites in Ising models on a countable set
• Minimal penalties for Gaussian model selection
• Adaptive density estimation of stationary \(\beta\)-mixing and \(\tau\)-mixing processes
• Consistent estimation of the basic neighborhood of Markov random fields
• Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms

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