Simulation-Based Inference Blueprint

Session

Neural Networks For Likelihood (Ratio) Estimation

26 Feb 2026, 15:30

13/2-005 (CERN)

10. Direct Likelihood Learning and Uncertainty Profiling with Normalizing Flows

Davide Valsecchi (ETH Zurich (CH))

26/02/2026, 15:30

Recent advances in Simulation-Based Inference (SBI) often rely on training classifiers to approximate likelihood ratios. However, direct density estimation using Normalizing Flows offers distinct advantages, particularly in the flexibility of the learned statistical model. In this presentation, we explore the use of Normalizing Flows to learn the likelihood function directly to infer physics...

11. Neural (Quasi-)Probabilistic Likelihood Ratio Estimation

Matthew Drnevich (New York University (US)), Stephen Jiggins (Deutsches Elektronen-Synchrotron (DE))

26/02/2026, 15:50

12. Discussion

26/02/2026, 16:10