The minimax learning rates of normal and Ising undirected graphical models
DOI10.1214/20-EJS1721zbMath1445.62069arXiv1806.06887OpenAlexW3037947134MaRDI QIDQ2192304
Tommy Reddad, Abbas Mehrabian, Luc P. Devroye
Publication date: 17 August 2020
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1806.06887
Ising modeldensity estimationMarkov random fieldgraphical modelmultivariate normalFano's lemmadistribution learning
Density estimation (62G07) Applications of graph theory (05C90) Minimax procedures in statistical decision theory (62C20) Applications of statistics to physics (62P35) Lattice systems (Ising, dimer, Potts, etc.) and systems on graphs arising in equilibrium statistical mechanics (82B20) Probabilistic graphical models (62H22)
Related Items (11)
Cites Work
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- Asymptotic methods in statistical decision theory
- Central limit theorems for empirical measures
- Exact recovery in the Ising blockmodel
- Complexity of random smooth functions on the high-dimensional sphere
- Convergence of estimates under dimensionality restrictions
- On the learnability of discrete distributions
- Handbook of Linear Algebra
- Efficiently learning mixtures of two Gaussians
- Rigorous results on the bipartite mean-field model
- Efficiently Learning Ising Models on Arbitrary Graphs
- A note on L1consistent estimation
- Testing Ising Models
- Information-Theoretic Limits of Selecting Binary Graphical Models in High Dimensions
- Estimation of analytic functions
- Understanding Machine Learning
- On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
- Mean Field Models for Spin Glasses
- Mean Field Models for Spin Glasses
- Introduction to nonparametric estimation
- Combinatorial methods in density estimation
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