Jan Boelts (Teusen)
Background in Computational Neuroscience
PhD in Machine Learning at University of Tübingen
AI Researcher at appliedAI TransferLab
Maintainer of the SBI package
TransferLab | appliedAI Institute for Europe
Homepage – Twitter handle –@janfiete
Git*hub|lab –janfb
Session
Simulators play a crucial role in scientific research, but accurately determining their parameters to reproduce observed data remains a significant challenge. Classical parameter inference methods often struggle due to the stochastic or black-box nature of these simulators. Simulation-based inference (SBI) offers a solution by enabling Bayesian parameter inference for simulation-based models: It only requires simulated data as input and returns a posterior distribution over suitable model parameters, including uncertainty estimates and parameter interactions. In this talk, we introduce SBI and present sbi, an open source library that serves as a central resource for SBI practitioners and researchers, offering state-of-the-art SBI algorithms, comprehensive documentation and tutorials.