Speaker
Mauro Pieroni
(Instituto de Estructura de la Materia (IEM - CSIC))
Description
Simulation-Based-Inference (SBI), also known as likelihood-free inference, is an alternative approach to Montecarlo techniques to perform Bayesian inference. SBI typically relies on Machine Learning (ML) methods to approximate the posterior distribution for some model parameters given the observed data. In the past years, SBI has been applied to a range of physics problems, including Gravitational Wave (GW) astronomy. In my talk, I will review the basics of SBI and discuss its application to the problem of detecting and characterizing GW Backgrounds (GWBs) with LISA.
Primary author
Mauro Pieroni
(Instituto de Estructura de la Materia (IEM - CSIC))
