Real-Time Gravitational-Wave Parameter Estimation Using Machine Learning
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Introduction to parameter estimation
Sampling
Challenges
Simulation-based inference
Normalizing flow
Simulation-based training
Embedding network
Group equivariant neural posterior estimation
Importance of method validation
Amortized inference
Quantitative comparison vs standard sampler
Summary comparison
Conclusions
Outlook
Taught by
Institute for Pure & Applied Mathematics (IPAM)