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.


Author:Rafael J. F. Marcondes
License:BSD License 2.0 with an added clause that if you use it in your work you must cite this user’s guide published in the arXiv repository as: Marcondes R. J. F., “EPIC - Easy Parameter inference in Cosmology: The user’s guide to the MCMC sampler”. arXiv:1712.00263 [astro-pm.IM]. See the LICENSE.txt file in the root directory of the source code for more details.

About the author

I’m a Brazilian engineer and physicist graduated from the Federal University of Sao Carlos (Engineering Physics, 2009), the University of Campinas (M.Sc. in Physics, 2012) and the University of Sao Paulo (Ph.D. in Physics, 2016). Besides developing EPIC, I have worked mainly with tests of interacting dark energy models using growth of structure and galaxy clusters data.

See the inSPIRE-HEP website to access my publications.


Version 1.0.4

  • Uses astropy.io.fits instead of pyfits for loading JLA (v4) files.

Version 1.0.2

New in this version:

  • Included instructions in documentation on how to show two or more results in the same triangle plot;
  • Added section “About the author” to documentation;
  • This changelog;
  • New background in html documentation favicon, uses readthedocs’ color;
  • Included arXiv eprint number of the PDF version of this documentation in license information;
  • Slightly reduced mathjax fontsize in html documentation;
  • Other minor changes to documentation.

Version 1.0.1

  • First release on PyPi