article · ArXiv.org · 2025
Scalable Bayesian inference on high-dimensional multivariate linear regression
Cao, Xuan, Lee, Kyoungjae
Cited by 0Open access
Abstract
We consider jointly estimating the coefficient matrix and the error precision matrix in high-dimensional multivariate linear regression models. Bayesian methods in this context often face computational challenges, leading to previous approaches that either utilize a generalized likelihood without ensuring the positive definiteness of the precision matrix or rely on maximization algorithms targeting only the posterior mode, thus failing to address uncertainty. In this work, we propose two Bayesian methods: an exact method and an approximate two-step method. We first propose an exact method based on spike and slab priors for the coefficient matrix and DAG-Wishart prior for the error precision matrix, whose computational complexity is comparable to the state-of-the-art generalized likelihood-based Bayesian method. To further enhance scalability, a two-step approach is developed by ignoring the dependency structure among response variables. This method estimates the coefficient matrix first, followed by the calculation of the posterior of the error precision matrix based on the estimated errors. We validate the two-step method by demonstrating (i) selection consistency and posterior convergence rates for the coefficient matrix and (ii) selection consistency for the directed acyclic graph (DAG) of errors. We demonstrate the practical performance of proposed methods through synthetic and real data analysis.
Cite this paper
Cao, X., & Lee, K. (2025). Scalable Bayesian inference on high-dimensional multivariate linear regression. ArXiv.org.
Cao, Xuan, and Kyoungjae Lee. “Scalable Bayesian inference on high-dimensional multivariate linear regression.” ArXiv.org, 2025.
Cao, Xuan, and Kyoungjae Lee. 2025. “Scalable Bayesian inference on high-dimensional multivariate linear regression.” ArXiv.org.
Cao, X. and Lee, K. (2025) ‘Scalable Bayesian inference on high-dimensional multivariate linear regression’, ArXiv.org.
@article{cao2025scalable,
title = {{Scalable Bayesian inference on high-dimensional multivariate linear regression}},
author = {Cao, Xuan and Lee, Kyoungjae},
journal = {ArXiv.org},
year = {2025}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.