In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can lead to confusion to both those learning and those familiar with Bayesian statistics. Moreover, the choice varies between the astronomy and statistics literature, too. In this paper, our goal is two-fold: (1) provide a reference that consolidates and clarifies terminology and notation across disciplines, and (2) outline practical guidance for Bayesian inference in astronomy. Highlighting both the astronomy and statistics literature, we cover topics such as notation, specification of the likelihood and prior distributions, inference using the posterior distribution, and posterior predictive checking. It is not our intention to introduce the entire field of Bayesian data analysis -- rather, we present a series of useful practices for astronomers who already have an understanding of the Bayesian "nuts and bolts" and wish to increase their expertise and extend their knowledge. Moreover, as the field of astrostatistics and astroinformatics continues to grow, we hope this paper will serve as both a helpful reference and as a jumping off point for deeper dives into the statistics and astrostatistics literature.

翻译：近二十年来，贝叶斯推断在天文学中已变得司空见惯。与此同时，算法选择、术语体系、符号约定以及贝叶斯推断的解读方式在不同天文学分支间存在显著差异，这给学习者和熟悉贝叶斯统计的研究者均造成困扰。此外，天文学文献与统计学文献之间的选择亦不尽相同。本文目标是双重的：（1）提供一份跨学科整合并澄清术语与符号的参考文献；（2）概述天文学贝叶斯推断的实用指南。我们兼顾天文学与统计学文献，涵盖符号约定、似然函数与先验分布设定、基于后验分布的推断及后验预测检验等主题。本文无意全面介绍贝叶斯数据分析领域——而是面向已掌握贝叶斯基本框架并希望提升专业水平与拓展知识的天文学家，提供一系列实用方法。同时，随着天文统计学与天文信息学领域的持续发展，我们期望本文既能作为实用参考，又能成为深入探索统计学与天文统计学文献的起点。