Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries

arXiv:2606. 04324v1 Announce Type: cross Abstract: One of the primary challenges in Bayesian inference on the parameters of a diffusion model from discrete observations is the unavailability of an analytical expression for the transition density function between consecutive observation times, which is needed to derive the likelihood function.