Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation

Cole, A.; Miller, B.K.; Witte, S.J.; Cai, X.; Grootes, M.W.; Nattino, F.; Weniger, C. (2022)

Article / Letter to editor

• Bayesian reasoning
• Machine learning
• Statistical sampling techniques
• Cosmological parameters from CMBR
• Astrophysics - cosmology and nongalactic astrophysics
• Astrophysics - instrumentation and methods for astrophysics
• Computer science - machine learning

• All authors: Cole, A.; Miller, B.K.; Witte, S.J.; Cai, X.; Grootes, M.W.; Nattino, F.; Weniger, C.
• Date: 2022
• Journal: Journal of Cosmology and Astroparticle Physics
• Volume: 2022
• Issue: 9
• DOI: doi:10.1088/1475-7516/2022/09/004
• Link: https://ui.adsabs.harvard.edu/abs/2022JCAP...09..004C