Solar Bayesian Analysis Toolkit --一种新的用于贝叶斯参数推断的马尔可夫链蒙特卡罗IDL代码
Solar Bayesian Analysis Toolkit -- a new Markov chain Monte Carlo IDL code for Bayesian parameter inference
摘要：
我们提出了太阳贝叶斯分析工具包（SoBAT），这是一个新的易于使用的工具，贝叶斯分析的观测数据，包括参数推断和模型比较。SoBAT旨在（但不限于）用于分析太阳观测数据。我们描述了一个新的交互式数据语言（IDL）的代码，旨在方便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（MCMC）抽样的使用允许有效地探索大的参数空间，并提供可靠的估计模型参数及其不确定性。不同模型的贝叶斯证据可用于定量比较。该代码进行测试，以证明其能够准确地恢复各种参数的概率分布。它的实际问题的应用证明了使用的结构和日冕环振荡的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
