Please see the tutorial notebooks for a discussion of the theory and a simple example.
Example 1: Bayesian linear regression using a neural network to generate samples from parameter posterior
Example 2: Neural ordinary differential equations as normalizing flow models to convert Gaussian noise into a bimodal mixture of Gaussians
Example 3: Variational inference of the generalized Lotka-Volterra model
