  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.
