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

中文翻译：

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集合采样器以解决无穷维反问题

我们引入了一种新的马尔可夫链蒙特卡罗（MCMC）采样器，用于无限维逆问题。我们的新采样器基于仿射不变整体采样器，它使用交互的助步器来适应目标分布的协方差结构。我们首次将此集成采样器扩展到无穷维函数空间，从而产生了一种高效的无梯度MCMC算法。因为我们的新集合采样器不需要梯度或后方协方差估计，所以它实现起来很简单并且广泛适用。