The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse Problems

ArXi:2505.12836v2 Announce Type: replace-cross We consider the problem of sampling from a product-of-experts-type model that encompasses many standard prior and posterior distributions commonly found in Bayesian imaging. We show that this model can be easily lifted into a novel latent variable model, which we refer to as a Gaussian latent machine. This leads to a general sampling approach that unifies and generalizes many existing sampling algorithms in the literature.