Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology-Reference-Cited by-同舟云学术

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

Published:2018-03-28 Issue:3 Volume:477 Page:2874-2885
ISSN:0035-8711
Container-title:Monthly Notices of the Royal Astronomical Society
language:en
Short-container-title:

Author:

Alsing Justin¹²,Wandelt Benjamin¹³⁴⁵,Feeney Stephen¹

Affiliation:

1. Center for Computational Astrophysics, Flatiron Institute, 162 5th Ave, New York City, NY 10010, USA

2. Imperial Centre for Inference and Cosmology, Department of Physics, Imperial College London, Blackett Laboratory, Prince Consort Road, London SW7 2AZ, UK

3. Institut d'Astrophysique de Paris (IAP), UMR 7095, CNRS UPMC Universite Paris 6, Sorbonne Universitse, 98bis boulevard Arago, F-75014 Paris, France

4. Institut Lagrange de Paris (ILP), Sorbonne Universitse, 98bis boulevard Arago, F-75014 Paris, France

5. Department of Physics and Astronomy, University of Illinois at Urbana-Champaign, 1002 W Green St, Urbana, IL 61801, USA

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

Link

http://academic.oup.com/mnras/article-pdf/477/3/2874/24802996/sty819.pdf

Reference65 articles.

1. Planck2015 results

2. Approximate Bayesian computation for forward modeling in cosmology

3. Generalized massive optimal data compression

4. Cosmological parameters, shear maps and power spectra from CFHTLenS using Bayesian hierarchical inference

5. The non-linear power spectrum of the Lyman alpha forest

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