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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.