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用于物理学中诸多需要检测与表征的问题。引力波天文学中的数据分析便是此类问题的典型例证。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，其中不确定性由实验测量的精度所决定。在过去几十年中，人们提出并应用了许多具体进展来解决各种不同的问题。本文提出了一种马尔可夫链蒙特卡洛（MCMC）算法，将其中许多概念整合到单一MCMC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）。本文描述了该采样器包，并通过多种用例展示了其能力。