scientific article; zbMATH DE number 7164764
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Publication:5214260
zbMath1446.62256arXiv1806.06209MaRDI QIDQ5214260
Publication date: 7 February 2020
Full work available at URL: https://arxiv.org/abs/1806.06209
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Random fields (60G60) Computational methods for problems pertaining to statistics (62-08) Random fields; image analysis (62M40) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of graph theory (05C90) Probabilistic graphical models (62H22)
Related Items (3)
Causal Structural Learning via Local Graphs ⋮ The dual PC algorithm and the role of Gaussianity for structure learning of Bayesian networks ⋮ Nonlinear directed acyclic graph estimation based on the kernel partial correlation coefficient
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Cites Work
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- Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs
- Geometry of the faithfulness assumption in causal inference
- The max-min hill-climbing Bayesian network structure learning algorithm
- Trek separation for Gaussian graphical models
- Causation, prediction, and search
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- High-dimensional consistency in score-based and hybrid structure learning
- PenPC : A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs
- High-dimensional learning of linear causal networks via inverse covariance estimation
- The small-world phenomenon
- A critical point for random graphs with a given degree sequence
- High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion
- Graph estimation with joint additive models
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