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Easy Parameter Inference in Cosmology (EPIC) is another Markov Chain MonteCarlo (MCMC) sampler for Cosmology. It is implemented in Python and providesBayesian parameter inference and model comparison based on the Bayesianevidence. The Parallel Tempering algorithm is included, which can help in theexploration of posterior distributions with two or more separated peaks.Adaptive routines for obtaining better efficiency with fine-tuned algorithmsare being developed and will be available in future versions. In this user'sguide, I give general instructions for installation and usage, includingexamples, and show how to modify the code in order to add new datasets andmodels.

EPIC - Easy Parameter Inference in Cosmology: The user's guide to the MCMC sampler