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
- 1. Welcome to the EPIC user’s guide!
- 2. How to install
- 3. The Cosmology module
- 4. The MCMC module
- 5. Acknowledgments
New in this version (May 2018):
- A new module for Cosmology calculations written from scratch, following an intensively object-oriented approach. This update facilitates the use of this code for Cosmology calculations separated from a MCMC simulation. Try it interactively with jupyter notebook!
- Observational data sets and likelihood calculations have also been reworked and improved. Choice of observations and models and new model set up process should be more transparent now.
- Parallel Tempering algorithm has been removed in this version and may return as a new implementation in a future release.
- Uses astropy.io.fits instead of pyfits for loading JLA (v4) files.
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.
- First release on PyPi