Eryn : A multi-purpose sampler for Bayesian inference

TL;DR

Eryn is a versatile MCMC toolkit designed for Bayesian inference, capable of handling diverse problems including parameter estimation and model selection, with applications demonstrated in gravitational-wave data analysis.

Contribution

This work introduces Eryn, a comprehensive, user-friendly MCMC package that integrates multiple advanced concepts for Bayesian inference in physics and astronomy.

Findings

Eryn successfully performs parameter estimation in gravitational-wave data.

The package supports trans-dimensional MCMC for complex model variations.

Eryn demonstrates versatility across different Bayesian inference problems.

Abstract

In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case. Bayesian inference has been very successful because this technique provides a representation of the parameters as a posterior probability distribution, with uncertainties informed by the precision of the experimental measurements. During the last couple of decades, many specific advances have been proposed and employed in order to solve a large variety of different problems. In this work, we present a Markov Chain Monte Carlo (MCMC) algorithm that integrates many of those concepts into a single MCMC package. For this purpose, we have built {\tt Eryn}, a user-friendly and multipurpose toolbox for Bayesian inference, which can be utilized for solving parameter estimation and model selection problems, ranging from simple inference questions, to those with large-scale model variation requiring trans-dimensional MCMC methods, like the LISA global fit problem. In this paper, we describe this sampler package and illustrate its capabilities on a variety of use cases.