Module-based regularization improves Gaussian graphical models when observing noisy data
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
Module-based regularization improves Gaussian graphical models when observing noisy data ; volume:9 ; number:1 ; day:18 ; month:3 ; year:2024 ; pages:1-12 ; date:12.2024
Applied network science ; 9, Heft 1 (18.3.2024), 1-12, 12.2024
- Creator
-
Neuman, Magnus
Calatayud, Joaquín
Tasselius, Viktor
Rosvall, Martin
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s41109-024-00612-8
- URN
-
urn:nbn:de:101:1-2405210847228.095409756861
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 14.08.2025, 10:49 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Neuman, Magnus
- Calatayud, Joaquín
- Tasselius, Viktor
- Rosvall, Martin
- SpringerLink (Online service)
Other Objects (12)
On robust Gaussian Graphical Modelling
Gaussian graphical modeling for spectrometric data analysis
Uniform inference in high-dimensional gaussian graphical models
Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models
The ‘un-shrunk’ partial correlation in Gaussian graphical models
An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images
An adaptive regularization by projection for noisy pseudodifferential equations of negative order
Noisy image magnification with total variation regularization and order-changed dictionary learning
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Noisy propagation of Gaussian states in optical media with finite bandwidth
An alternating multiple residual Wasserstein regularization model for Gaussian image denoising
Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors
On robust Gaussian Graphical Modelling
Gaussian graphical modeling for spectrometric data analysis
Uniform inference in high-dimensional gaussian graphical models
Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models
The ‘un-shrunk’ partial correlation in Gaussian graphical models
An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images
An adaptive regularization by projection for noisy pseudodifferential equations of negative order
Noisy image magnification with total variation regularization and order-changed dictionary learning
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Noisy propagation of Gaussian states in optical media with finite bandwidth
An alternating multiple residual Wasserstein regularization model for Gaussian image denoising
Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors
On robust Gaussian Graphical Modelling
Gaussian graphical modeling for spectrometric data analysis
Uniform inference in high-dimensional gaussian graphical models
Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models
The ‘un-shrunk’ partial correlation in Gaussian graphical models
An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images
An adaptive regularization by projection for noisy pseudodifferential equations of negative order
Noisy image magnification with total variation regularization and order-changed dictionary learning
Exploring non-Gaussian sea ice characteristics via observing system simulation experiments
Noisy propagation of Gaussian states in optical media with finite bandwidth
An alternating multiple residual Wasserstein regularization model for Gaussian image denoising