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Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding
Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, allowing a pretrained diffusion prior to be reused across measurement models. However, its likelihood update often relies on iterative MCMC, which can hi
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