Abstract: In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems.

Abstract: For Bayesian network structure learning with continuous data, traditional methods typically require data discretization or assume that the data follows a Gaussian distribution. However, the ...