Jan Teusen

Jan initially immersed himself in the realms of cognitive science and computational neuroscience. However, he couldn’t resist the siren call of Bayesian machine learning, and his PhD evolved into a mission to enhance the user-friendliness of this complex field. He set out to bridge cutting-edge methods with user-friendly software, making the world of simulation-based inference more accessible for practitioners. In 2024, he joined the TransferLab, ready to continue his journey of making advanced methodologies approachable and transformative.

Jan's work

Software

sbi: the simulation-based inference toolkit

sbi is a Python package for Bayesian parameter inference on simulators. It implements state-of-the-art algorithms and comes with …

Jan Teusen Maternus Herold Faried Abu Zaid
Simulation-Based Inference
Pill

Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models

In Simulation-based inference (SBI) conditional density estimators are used to approximate the posterior distribution from simulated data. …

Jan Teusen
Simulation-Based Inference
Seminar

Mind the Gap: Methods and Applicability of Simulation-Based Inference

Jan Teusen (né Boelts), AI Researcher at appliedAI Institute, will give a gentle introduction to simulation-based inference (SBI), along …

Jan Teusen
Simulation-Based Inference
Pill

Adversarial robustness of amortized Bayesian inference

The idea of amortized Bayesian inference is to rapidly perform inference for new data points by initially investing cost in training a …

Jan Teusen
Simulation-Based Inference
Training

Introduction to Simulation-based Inference

Embrace the challenges of intractable likelihoods with simulation-based inference. A half-day workshop introducing the concepts …