Simulation-based Inference for Gravitational Wave Astronomy

Kyle Cranmer
New York University

I will briefly review the taxonomy of simulation-based inference techniques developed in the last few years.
The taxonomy will include branching on frequentist vs. Bayesian, learned or engineered summary statistics, densities vs. density ratios, and amortized vs. sequential approaches. I will then try to place the concerns of gravitational wave astronomy in this framing. I will also discuss briefly the role of inductive bias (e.g. symmetries) for approaches based on neural networks.

Presentation (PDF File)

Back to Workshop III: Source inference and parameter estimation in Gravitational Wave Astronomy