Ensemble sampler for infinite-dimensional inverse problems

We introduce a new Markov chain Monte Carlo sampler for infinite-dimensional Bayesian inverse problems. The new sampler is based on the affine invariant ensemble sampler, which we extend for the first time to function spaces. The new sampler is more efficient than preconditioned Crank-Nicolson, yet it requires no gradient information or posterior covariance information, making the sampler broadly applicable.