Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding
A learning-based framework is proposed to replace this MCMC step by reformulating both Gibbs updates as Gaussian denoising problems and implementing them through ODE diffusion, resulting in an alternative to MCMC-based split Gibbs at lower likelihood-update cost.
Yi Zhang, Rui Guo, Meng-Chu Xu et al.
· 0 citations