Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference

ArXi:2605.29467v1 Announce Type: cross Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be preserved. We identify five factor-graph primitives: a bilinear factor, an exponential link, a Gamma prior, a Gaussian likelihood, and an equality node, and prove that any model composed from them admits closed-form variational message passing.