Search Results for author:
Found 4 papers, 2 papers with code
Asynchronous and Distributed Data Augmentation for Massive Data Settings
The extended DA algorithm is indexed by a parameter $r \in (0, 1)$ and is called Asynchronous and Distributed (AD) DA with the original DA as its parent.
Consistent Bayesian Sparsity Selection for High-dimensional Gaussian DAG Models with Multiplicative and Beta-mixture Priors
Estimation of the covariance matrix for high-dimensional multivariate datasets is a challenging and important problem in modern statistics.
Statistics Theory Methodology Statistics Theory
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
In direct contrast to the parallel work in the Gaussian setting however, this new convex pseudo-likelihood framework has not leveraged the extensive array of methods that have been proposed in the machine learning literature for convex optimization.
A convex pseudo-likelihood framework for high dimensional partial correlation estimation with convergence guarantees
As none of the popular methods proposed for solving pseudo-likelihood based objective functions have provable convergence guarantees, it is not clear if corresponding estimators exist or are even computable, or if they actually yield correct partial correlation graphs.
