Cosmological parameter inference with Bayesian statistics. (arXiv:1903.11127v3 [astro-ph.CO] UPDATED)

Cosmological parameter inference with Bayesian statistics. (arXiv:1903.11127v3 [astro-ph.CO] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Padilla_L/0/1/0/all/0/1">Luis E. Padilla</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tellez_L/0/1/0/all/0/1">Luis O. Tellez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Escamilla_L/0/1/0/all/0/1">Luis A. Escamilla</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Vazquez_J/0/1/0/all/0/1">J. Alberto Vazquez</a>

Bayesian statistics and Markov Chain Monte Carlo (MCMC) algorithms have found
their place in the field of Cosmology. They have become important mathematical
and numerical tools, especially in parameter estimation and model comparison.
In this paper, we review some fundamental concepts to understand Bayesian
statistics and then introduce MCMC algorithms and samplers that allow us to
perform the parameter inference procedure. We also introduce a general
description of the standard cosmological model, known as the $Lambda$CDM
model, along with several alternatives, and current datasets coming from
astrophysical and cosmological observations. Finally, with the tools acquired,
we use an MCMC algorithm implemented in python to test several cosmological
models and find out the combination of parameters that best describes the
Universe.

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