The resolution of many large-scale inverse problems using MCMC methods
requires a step of drawing samples from a high dimensional Gaussian
distribution. While direct Gaussian sampling techniques, such as those based on
Cholesky factorization, induce an excessive numerical complexity and memory
requirement, sequential coordinate sampling methods present a low rate of
convergence. Based on the reversible jump Markov chain framework, this paper
proposes an efficient Gaussian sampling algorithm having a reduced computation
cost and memory usage. The main feature of the algorithm is to perform an
approximate resolution of a linear system with a truncation level adjusted
using a self-tuning adaptive scheme allowing to achieve the minimal computation
cost. The connection between this algorithm and some existing strategies is
discussed and its efficiency is illustrated on a linear inverse problem of
image resolution enhancement.

本文介绍了基于可逆跳 Markov 链的高效 Gaussian 采样算法，该算法通过自适应方案调整截断级别实现线性系统的近似解法，并在图像分辨率增强的线性反问题上得到了验证。