论文标题
与统计分析的任意tokamak政权的单个高斯过程方法
Single Gaussian Process Method for Arbitrary Tokamak Regimes with a Statistical Analysis
论文作者
论文摘要
高斯过程回归（GPR）是一种基于输入数据来推断概况的贝叶斯方法。该技术在融合社区中的流行程度越来越高，因为它比传统的拟合技术具有许多优势，包括内在的不确定性量化和过度拟合的鲁棒性。这项工作调查了一种新方法，即变更点方法，用于处理不同托卡马克政权中发现的不同长度尺度。还研究了学生的T-分布在贝叶斯可能性概率上的使用，并证明在提供许多异常值的配置文件方面具有优势。为了比较不同的方法，使用分析配置文件生成的合成数据来创建一个数据库，从而实现了哪种方法的定量统计比较。使用更改点方法的完整贝叶斯方法，Matérn内核可提前概率，并且可能证明学生的T-分布可带来最佳效果。
Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student's t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Matérn kernel for the prior probability, and Student's t-distribution for the likelihood is shown to give the best results.
