Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science.

Efficient approximation and sampling methods of intractable distributions plays a key role in probabilistic machine learning, especially Bayesian inference.

Our method extends recent developments in simulation-based inference (SBI) based on normalizing flows to Bayesian hierarchical models.