论文标题
太阳能贝叶斯分析工具包 - 新的马尔可夫链蒙特卡洛IDL代码用于贝叶斯参数推理
Solar Bayesian Analysis Toolkit -- a new Markov chain Monte Carlo IDL code for Bayesian parameter inference
论文作者
论文摘要
我们介绍了太阳能贝叶斯分析工具包（SOBAT），它是一种易于使用的工具，用于贝叶斯分析观测数据，包括参数推理和模型比较。 SOBAT针对（但不限）用于分析太阳观测数据。我们描述了一种新的交互式数据语言（IDL）代码，旨在促进用户提供的模型与数据的比较。贝叶斯推论允许考虑事先信息。马尔可夫链蒙特卡洛（MCMC）采样的使用可以有效探索大型参数空间，并提供了模型参数及其不确定性的可靠估计。贝叶斯的不同模型证据可用于定量比较。对代码进行了测试，以证明其准确恢复各种参数概率分布的能力。使用冠状环的结构和振荡研究证明了它在实际问题上的应用。
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 Interactive Data Language (IDL) code designed to facilitate the comparison of user-supplied model with data. Bayesian inference allows prior information to be taken into account. The use of Markov chain Monte Carlo (MCMC) 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.
