posterior-sampling

This paper introduces Christoffel-DPS, a distribution-free framework for optimal sensor placement in diffusion posterior sampling that outperforms classical Gaussian-based methods. It provides theoretical guarantees and practical improvements for reconstructing states from complex, non-Gaussian distributions using generative models.