Cosmological Parameter Inference with Bayesian Statistics

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