CDS offers a practical way to sample from difficult distributions by combining two proven techniques—Parallel Tempering for initial exploration and exact diffusion dynamics for refinement—without requiring neural network training.
This paper introduces Conditional Diffusion Sampling (CDS), a new method for sampling from complex probability distributions that combines Parallel Tempering with diffusion-based transport.