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Non-Gaussian inference from non-linear and non-Poisson biased distributed data

Published online by Cambridge University Press:  01 July 2015

Metin Ata ,
Francisco-Shu Kitaura  and
Volker Müller
Metin Ata
Affiliation:
Leibniz Institute for Astrophysics (AIP), An der Sternwarte 16, 14482 Potsdam email: mata@aip.de
Francisco-Shu Kitaura
Affiliation:
Leibniz Institute for Astrophysics (AIP), An der Sternwarte 16, 14482 Potsdam email: mata@aip.de
Volker Müller
Affiliation:
Leibniz Institute for Astrophysics (AIP), An der Sternwarte 16, 14482 Potsdam email: mata@aip.de
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Abstract

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We study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based on N-body simulations.

Keywords

cosmology: large-scale-structuresmethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 258 - 261
Copyright
Copyright © International Astronomical Union 2015 

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