  Graph Networks (GNs) enable the fusion of prior knowledge and relational
reasoning with flexible function approximations. In this work, a general
GN-based model is proposed which takes full advantage of the relational
modeling capabilities of GNs and extends these to probabilistic modeling with
Variational Bayes (VB). To that end, we combine complementary pre-existing
approaches on VB for graph data and propose an approach that relies on
graph-structured latent and conditioning variables. It is demonstrated that
Neural Processes can also be viewed through the lens of the proposed model. We
show applications on the problem of structured probability density modeling for
simulated and real wind farm monitoring data, as well as on the meta-learning
of simulated Gaussian Process data. We release the source code, along with the
simulated datasets.

Computational Engineering, Finance, and Science;Machine Learning;Machine Learning