A rare occurrence of a Bayesian statistics paper in Nature with this “State estimation of a physical system with unknown governing equations” by Course and Nair. A variational Bayes modelling of a state system observed with noise, but without a physical model on the state (SDE) evolution itself. Which means a prior is set on a non-parametric or neural representation of the drift and a linear approximation is used for the variational approximation, leading to a Gaussian process as the approximate distribution. While this applies to highly complex models, like orbiting black holes, it is somewhat a surprise to meet this application of variational inference in a prestigious general science journal like Nature. (The picture above was taken on the train from Marseille at the end of the Bayes Fall school.)
“The approach is based on a technique called Bayesian inference, which is used widely, but which can be computationally challenging for complex systems.” B. Keith