Structured Priors for Multivariate Data

Background: The performance of a learned prior regularization term depends on its structural fit to the target distribution, which is case-dependent.

Question / Future Work: The learned RealNVP flow prior systematically improved performance over the isotropic Gaussian prior on GO cases. Future work should investigate extending this concept by incorporating structured priors for multivariate settings, which are necessary for modeling complex spatio-temporal dependencies.