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SUMMARY:Beyond Likelihoods: Bayesian Parameter Inference for Black-Box Sim
 ulators with sbi - Jan Boelts (Teusen)\, Maternus Herold
DTSTART;TZID=Europe/Warsaw:20250819T153000
DTEND;TZID=Europe/Warsaw:20250819T170000
DTSTAMP:20260911T043038Z
UID:pretalx-euroscipy-2025-MU9HAJ@pretalx.com
DESCRIPTION:Do you spend time tuning parameters for complex scientific sim
 ulators? Perhaps you use grid search or optimization to match parameters t
 o data. These find a best-fit set\, but often don't reveal your confidence
  or if other parameters also fit. This uncertainty is crucial for reliable
  conclusions.\nThis tutorial introduces Simulation-Based Inference (SBI)\,
  a modern technique tackling this challenge. Unlike traditional Bayesian i
 nference methods (like MCMC) that require mathematical likelihood function
 s\, SBI works directly with your simulator's outputs. Using recent advance
 s in probabilistic ML\, it estimates the probability distribution of param
 eter values consistent with your observations\, even for complex "black-bo
 x" simulators. It provides not just a single best guess\, but full paramet
 er distributions representing parameter uncertainties and potential intera
 ctions.\nIn this hands-on tutorial using the `sbi` Python package\, you'll
  learn the practical steps: setting up the problem\, running SBI for param
 eter distributions\, and checking result reliability. We will cover differ
 ent SBI techniques and how to apply them.\nIf you are a scientist or engin
 eer using Python for simulations\, or just interested in probabilistic inf
 erence methods\, this session is for you. You will learn to obtain more re
 liable and interpretable results by quantifying uncertainty and understand
 ing how parameters interact within your model.\n[Link to material](https:/
 /github.com/janfb/euroscipy-2025-sbi-tutorial)
LOCATION:Room 1.38 (Ground Floor)
URL:https://pretalx.com/euroscipy-2025/talk/MU9HAJ/
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