Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint
Paul Hagemann, Johannes Hertrich, Gabriele Steidl · SIAM/ASA Journal on Uncertainty Quantification · 2022
Abstract. To overcome topological constraints and improve the expressiveness of normalizing flow architectures, Wu, Köhler, and Noé introduced stochastic normalizing flows which combine deterministic, learnable flow transformations with stochastic sampling methods. In this paper, we consider stochastic normalizing flows from a Markov chain point of view. In particular, we replace transition densities by general Markov kernels and establish proofs via Radon–Nikodym derivatives, which allows us to incorporate distributions without densities in a sound way. Further, we generalize the results for sampling from posterior distributions as required in inverse problems. The performance of the proposed conditional stochastic normalizing flow is demonstrated by numerical examples.