Ensemble sampler for infinite-dimensional inverse problems

We introduce a new Markov chain Monte Carlo (MCMC) sampler for infinite-dimensional inverse problems. Our is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt covariance structure of target distribution. extend this first time function spaces, yielding highly efficient gradient-free MCMC algorithm. Because our does not require gradients or posterior estimates, it simple implement and broadly applicable.