Applications of Conjugate Gradient in Bayesian computation
Methodology
2023-08-30 v1
Abstract
Conjugate gradient is an efficient algorithm for solving large sparse linear systems. It has been utilized to accelerate the computation in Bayesian analysis for many large-scale problems. This article discusses the applications of conjugate gradient in Bayesian computation, with a focus on sparse regression and spatial analysis. A self-contained introduction of conjugate gradient is provided to facilitate potential applications in a broader range of problems.
Cite
@article{arxiv.2308.14828,
title = {Applications of Conjugate Gradient in Bayesian computation},
author = {Lu Zhang},
journal= {arXiv preprint arXiv:2308.14828},
year = {2023}
}
Comments
7 pages. In Wiley StatsRef: Statistics Reference Online (2023). This paper was originally published on Wiley StatsRef: Statistics Reference Online on December 15 2022. The reason for reuploading it on arXiv is to enhance its visibility and accessibility