Year: 2021
Type: preprint
Abstract: Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method -- called group equivariant neural posterior estimation (GNPE) -- is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, International Max Planck Research School for Environmental, Cellular and Molecular Microbiology, Bundesministerium für Bildung und Forschung, Centre National de la Recherche Scientifique, International Max Planck Research School for Advanced Methods in Process and Systems Engineering, Istituto Nazionale di Fisica Nucleare, Science and Technology Facilities Council
OpenAlex ID: W3216693867