A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions
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The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patterns are crucial to he... ...
