Reconsidering Dependency Networks from an Information Geometry Perspective

arXiv:2604.01117v1 Announce Type: new Abstract: Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional distributions through pseudo-Gibbs sampling. Despite their computational adv