In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site Metropolis updates. We show how fast, approximate models can be leveraged to improve the MCMC sampling efficiency.

翻译：在本章中，我们致力于解决伴随计算密集型正向模型的贝叶斯逆问题中后验分布的探索挑战。我们考察了多种多元提议分布，并将其与单点梅特罗波利斯更新进行了比较。我们展示了如何利用快速近似模型来提升马尔可夫链蒙特卡洛采样的效率。