EPIC - Easy Parameter Inference in Cosmology: The user's guide to the MCMC sampler
Abstract
Easy Parameter Inference in Cosmology (EPIC) is another Markov Chain Monte Carlo (MCMC) sampler for Cosmology. It is implemented in Python and provides Bayesian parameter inference and model comparison based on the Bayesian evidence. The Parallel Tempering algorithm is included, which can help in the exploration of posterior distributions with two or more separated peaks. Adaptive routines for obtaining better efficiency with fine-tuned algorithms are being developed and will be available in future versions. In this user's guide, I give general instructions for installation and usage, including examples, and show how to modify the code in order to add new datasets and models.
Cite
@article{arxiv.1712.00263,
title = {EPIC - Easy Parameter Inference in Cosmology: The user's guide to the MCMC sampler},
author = {Rafael J. F. Marcondes},
journal= {arXiv preprint arXiv:1712.00263},
year = {2018}
}
Comments
Version 1.4 with new features, including the possibility to resume simulation from the graphical interface and updated data of CMB distance priors. HTML version of this guide at https://epic-code.readthedocs.io/