Applications of Conjugate Gradient in Bayesian computation

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.

arXiv comment7 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

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