Applications of Conjugate Gradient in Bayesian computation

TL;DR

This paper explores how the conjugate gradient algorithm can be effectively applied to enhance Bayesian computation, especially in large-scale sparse regression and spatial analysis problems.

Contribution

It provides a comprehensive overview of conjugate gradient applications in Bayesian analysis and introduces its potential for broader problem domains.

Findings

Conjugate gradient accelerates Bayesian computations in large-scale problems.

Effective in sparse regression and spatial analysis contexts.

Provides foundational understanding for future applications.

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.