Fast likelihood-free cosmology with neural density estimators and active learning

Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational effects that can be successfully included in the simulations. The key challenge for likelihood-free applications in cosmolo...

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Veröffentlicht in:	arXiv.org 2019-02
Hauptverfasser:	Alsing, Justin, Charnock, Tom, Feeney, Stephen, Wandelt, Benjamin
Format:	Artikel
Sprache:	eng
Schlagworte:	Active learning Bayesian analysis Computer simulation Cosmology Density Estimators Machine learning Parameters Physics - Cosmology and Nongalactic Astrophysics Simulation Statistical inference
Online-Zugang:	Volltext
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