Projects
At BayesOps, we develop robust software and novel neural architectures for end-to-end Bayesian modeling. The lab is generously supported by the National Science Foundation (NSF).
Highlighted Projects
BayesFlow is a Python library for simulation-based inference with generative AI. It provides users and researchers with a user-friendly API for rapid Bayesian workflows, a rich collection of neural architectures, and multi-backend support via Keras3.
We are developing neural networks that will allow researchers to fit thousands of cognitive models. The project’s focus on robust inference and benchmarking aims to strenghten the emerging infrastructure for model-based analysis in the behavioral sciences.
Applications
In a first-of-its-kind application, we use simulation-based inference for modeling human motion while experiencing shared virtual worlds, paving the way for model-based research on human interaction with the built environment.