This talk introduces a novel approach that bridges SBI and Pyro to enable simulation-based hierarchical Bayesian inference, combining SBI’s ability to handle intractable simulators with Pyro’s expressive power for complex hierarchical structures.

This talk covers SBI, a method for Bayesian parameter inference using simulated data, and introduces the sbi library, an open-source tool for practitioners and researchers.

Internal seminar at TransferLab discussing recent advances in simulation-based inference methods and their practical applications across scientific domains.

Invited presentation at the Bernstein Center for Computational Neuroscience Berlin on using SBI methods for parameter inference in models of neural circuits and behavior.