EPSIE is a parallelized Markov chain Monte Carlo (MCMC) sampler for Bayesian inference. It is meant for problems with complicated likelihood topology, including multimodal distributions. It has support for both parallel tempering and nested transdimensional problems. It was originally developed for gravitational-wave parameter estimation, but can be used for any Bayesian inference problem requring a stochastic sampler.
EPSIE is in many ways similar to emcee and other bring-your-own-likelihood Python-based samplers. The primary difference from emcee is EPSIE is not an ensemble sampler; i.e., the Markov chains used by EPSIE do not attempt to share information between each other. Instead, to speed convergence, multiple jump proposal classes are offered that can be customized to the problem at hand. These include adaptive proposals that attempt to learn the shape of the distribution during a burn-in period. The user can also easily create their own jump proposals.
For more information, see the documentation at: https://cdcapano.github.io/epsie
If you use EPSIE in your work, please cite DOI 10.5281/zenodo.5717225 for the latest version, or the DOI specific to the release you used. Authorship, citation format, and DOI for all versions are available at Zenodo.