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.

翻译：近年来，贝叶斯推断方法已广泛应用于物理学中需要探测和表征的诸多问题中。引力波天文学的数据分析便是这类应用的典型代表。贝叶斯推断之所以非常成功，是因为该技术能够将参数表示为后验概率分布，其不确定性由实验测量的精度所决定。在过去的几十年中，人们提出并应用了许多具体的进展来解决各种不同的问题。在这项工作中，我们提出了一种马尔可夫链蒙特卡罗（MCMC）算法，该算法将许多这样的概念整合到一个单一的MCMC软件包中。为此，我们构建了{\tt Eryn}，一个用户友好且多用途的贝叶斯推断工具箱，可用于解决参数估计和模型选择问题——从简单的推断问题到需要跨维度MCMC方法的大规模模型变化问题（如LISA全局拟合问题）。本文描述了这一采样器软件包，并通过多种用例展示了其能力。