Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference. (arXiv:2304.11134v1 [stat.ML])

This paper introduces a stochastic plug-and-play (PnP) sampling algorithm
that leverages variable splitting to efficiently sample from a posterior
distribution. The algorithm based on split Gibbs sampling (SGS) draws
inspiration from the alternating direction method of multipliers (ADMM). It
divides the challenging task of posterior sampling into two simpler sampling
problems. The first problem depends on the likelihood function, while the
second is interpreted as a Bayesian denoising problem that can be readily
carried out by a deep generative model. …

algorithm arxiv bayesian bayesian inference denoising distribution embedding function generative gibbs inference likelihood paper pnp posterior sampling sgs stochastic