Invited Talk: Deep Posterior Sampling

Abstract

We show how generative models from machine learning can be used to perform Bayesian inversion on large-scale problems. Specifically, we train a Wasserstein GAN on 3D CT data and use it to sample from the posterior distribution. We demonstrate the ability of such Bayesian inversion by computing estimators and performing a hypothesis test.