Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
representative citing papers
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
citing papers explorer
-
Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
Bilby introduces a user-friendly Python library for accurate Bayesian inference on gravitational-wave signals from compact binaries and other sources, including hierarchical population modeling.
-
DustNET: enabling machine learning and AI models of dusty plasmas
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.