Efficient Bayesian regularization for graphical model selection
From MaRDI portal
Publication:1738143
DOI10.1214/17-BA1086zbMath1416.62317WikidataQ101122075 ScholiaQ101122075MaRDI QIDQ1738143
Suprateek Kundu, Veera Baladandayuthapani, Bani. K. Mallick
Publication date: 29 March 2019
Published in: Bayesian Analysis (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ba/1531274648
selection consistencyshrinkage priorscovariance selectionCholesky-based regularizationjoint penalized credible regions
Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20) Bayesian inference (62F15)
Related Items
Bayesian estimation for longitudinal data in a joint model with HPCs, Scalable Bayesian matrix normal graphical models for brain functional networks, A loss‐based prior for Gaussian graphical models, Bayesian estimation of correlation matrices of longitudinal data, Maximum pairwise Bayes factors for covariance structure testing, Efficient Bayesian regularization for graphical model selection, Post-processing posteriors over precision matrices to produce sparse graph estimates, Semi-parametric Bayes regression with network-valued covariates
Uses Software
Cites Work
- Unnamed Item
- A unified approach to model selection and sparse recovery using regularized least squares
- Sparse inverse covariance estimation with the graphical lasso
- Bayesian structure learning in sparse Gaussian graphical models
- The performance of covariance selection methods that consider decomposable models only
- Simple marginally noninformative prior distributions for covariance matrices
- Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models
- Covariance regularization by thresholding
- Conjugate priors for exponential families
- Hyper Markov laws in the statistical analysis of decomposable graphical models
- Efficient Bayesian regularization for graphical model selection
- Least angle regression. (With discussion)
- The Lasso problem and uniqueness
- Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care
- Experiments in stochastic computation for high-dimensional graphical models
- High-dimensional graphs and variable selection with the Lasso
- Efficient estimation of covariance selection models
- Nonparametric estimation of large covariance matrices of longitudinal data
- Adaptive Thresholding for Sparse Covariance Matrix Estimation
- Objective Bayesian model selection in Gaussian graphical models
- Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models
- Model selection and estimation in the Gaussian graphical model
- The horseshoe estimator for sparse signals
- Bayesian analysis of matrix normal graphical models
- Parsimonious Covariance Matrix Estimation for Longitudinal Data
- Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions
- Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
- Decomposable graphical Gaussian model determination
- Cholesky decomposition of a hyper inverse Wishart matrix
- Bayes Variable Selection in Semiparametric Linear Models
- Partial Correlation Estimation by Joint Sparse Regression Models
- Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data
- Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective
- Sampling decomposable graphs using a Markov chain on junction trees
- A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models
- Detecting differential gene expression with a semiparametric hierarchical mixture method
