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

Abstract

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.

Proceedings of IAU 306 Symposium, Statistical Challenges of the 21st Century Cosmology