# 1. Welcome to the EPIC user’s guide!¶

Easy Parameter Inference in Cosmology (EPIC) is my implementation in Python of a MCMC code for Bayesian inference of parameters of cosmological models and model comparison via the computation of Bayesian evidences.

## 1.1. Why EPIC?¶

I started to develop EPIC as a means of learning how parameter inference can be made with Markov Chain Monte Carlo, rather than trying to decipher other codes or using them as black boxes. The program has fulfilled this purposed and went on to incorporate a few cosmological observables that I have actually employed in some of my publications. Now I release this code in the hope it can be useful to students seeking to learn some of the methods used in Observational Cosmology and even to use it for their own work. It still lacks some important features. A Boltzmann solver is not available. It is possible that I will integrate it with CLASS [1] to make it more useful for advanced research. Stay tuned for more.

On the other hand, development is active and with recent versions it is now possible not only to use EPIC’s Cosmology Calculator but also run MCMC simulations from a nice graphical interface. You can also, check out these other features:

- Cross-platform: the code runs on Python 3 in any operating system.
- EPIC features a Cosmology Calculator that also supports a few models other than the standard \(\Lambda\text{CDM}\) model. The list of models include interacting dark energy and some dark energy equation-of-state parametrizations. The code can output some key distance calculations and compare them between different models over a range of redshifts, generating extensively customizable plots.
- It uses Python’s
`multiprocessing`

library for evolution of chains in parallel in MCMC simulations. The separate processes can communicate with each other through some`multiprocessing`

utilities, which made possible the implementation of the Parallel Tempering algorithm. [2] This method is capable of detecting and accurately sampling posterior distributions that present two or more separated peaks. - Convergence between independent chains is tested with the multivariate version of the Gelman and Rubin test, a very robust method.
- Also, the plots are beautiful and can be customized to a great extent directly from the command line or from the graphical interface, without having to change the code. You can view triangle plots with marginalized distributions of parameters, predefined derived parameters, two-dimensional joint-posterior distributions, autocorrelation plots, cross-correlation plots, sequence plots, convergence diagnosis and more.

Try it now!

Footnotes

[1] | Lesgourgues, J. “The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview”. arXiv:1104.2932 [astro-ph.IM]; Blas, D., Lesgourgues, J., Tram, T. “The Cosmic Linear Anisotropy Solving System (CLASS). Part II: Approximation schemes”. Journal of Cosmology and Astroparticle Physics 07 (2011) 034. |

[2] | Removed in current version. If you need to use Parallel Tempering, please use version 1.0.4 of EPIC. |