Bayesian inference is used in many scientific areas as a conceptually well-founded data analysis framework. In this paper, we give a brief introduction to Bayesian probability theory and its application to the tomography problem in fusion research by means of a Gaussian process prior. This Gaussian process tomography (GPT) method is used for reconstruction of the local soft X-ray (SXR) emissivity in WEST and EAST based on line-integrated data. By modeling the SXR emissivity field in a poloidal cross-section as a Gaussian process, Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time feedback information on impurity transport and for fast MHD control. In addition, the Bayesian formulism allows for uncertainty analysis of the inferred emissivity.

中文翻译：

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高斯过程断层扫描的贝叶斯数据分析

贝叶斯推理在许多科学领域中被用作概念上有充分根据的数据分析框架。在本文中，我们通过高斯过程先验对贝叶斯概率论及其在融合研究中的层析成像问题中的应用进行了简要介绍。这种高斯过程断层扫描 (GPT) 方法用于基于线集成数据重建 WEST 和 EAST 的局部软 X 射线 (SXR) 发射率。通过将极向截面中的 SXR 发射率场建模为高斯过程，贝叶斯 SXR 层析成像可以以稳健且极快的方式进行。由于该算法的执行时间短，GPT 是提供有关杂质传输的实时反馈信息和快速 MHD 控制的重要候选者。此外，