Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers

@article{Ata2014BayesianIO,
title={Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers},
author={Metin Ata and Francisco-Shu Kitaura and Volker Muller},
journal={Monthly Notices of the Royal Astronomical Society},
year={2014},
volume={446},
pages={4250-4259}
}
• Published 11 August 2014
• Physics
• Monthly Notices of the Royal Astronomical Society
We present a Bayesian reconstruction algorithm to generate unbiased samples of the underlying dark matter field from galaxy redshift data. Our new con tribution consists of implementing a non-Poisson likelihood including a deterministic non-linear and scale-dependent bias. In particular we present the Hamiltonian equations of motions for the negative binomial (NB) probability distribution function. This permits us to efficiently sample the posterior distribution function of density fields given…
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