We present the Solar Bayesian Analysis Toolkit (SoBAT), which is a new easy to use tool for Bayesian analysis of observational data, including parameter inference and model comparison. SoBAT is aimed (but not limited) to be used for the analysis of solar observational data. We describe a new IDL code designed to facilitate the comparison of a user-supplied model with data. Bayesian inference allows prior information to be taken into account. The use of Markov Chain Monte Carlo sampling allows efficient exploration of large parameter spaces and provides reliable estimation of model parameters and their uncertainties. The Bayesian evidence for different models can be used for quantitative comparison. The code is tested to demonstrate its ability to accurately recover a variety of parameter probability distributions. Its application to practical problems is demonstrated using studies of the structure and oscillation of coronal loops.

我们介绍了Solar Bayesian Analysis Toolkit（SoBAT），这是一种易于使用的新工具，用于贝叶斯观测数据分析，包括参数推断和模型比较。SoBAT旨在（但不限于）用于太阳观测数据的分析。我们描述了一种新的IDL代码，该代码旨在促进用户提供的模型与数据的比较。贝叶斯推断允许考虑先验信息。马尔可夫链蒙特卡罗采样的使用可以有效地探索大参数空间，并提供对模型参数及其不确定性的可靠估计。不同模型的贝叶斯证据可用于定量比较。该代码经过测试，证明其能够准确恢复各种参数概率分布。