Introduction to simulation-based inference

Diagram of likelihood-free inference methods from PNAS 117 (48) 30055-30062 (2020).


In many scientific fields, ranging from astrophysics to particle physics and neuroscience, simulators for dynamical systems generate a massive amount of data. One of the crucial tasks scientists are spending their precious time on is comparing observational data to the aforementioned simulations in order to infer physically relevant parameters and their uncertainties based on the model embedded in the simulator. This poses a problem because the likelihood function for realistic simulations of complex physical systems is intractable. Simulation-based inference techniques attack this problem using machine learning tools and probabilistic programming. I will give an overview of the problem and explain the application of such methods using examples.

Jul 6, 2021 4:00 PM — 5:00 PM
Invited seminar
Center for Brain Science (CBS), RIKEN, Wako
Enrico Rinaldi
Enrico Rinaldi
Research Scientist

My research interests include artificial intelligence and quantum computing applied to particle physics and quantum many-body systems.