| 李红莉,陈志文,张祖杨,赵志浩,黄强先,张连生,程荣俊.接受-拒绝算法的贝叶斯不确定度评定[J].电子测量与仪器学报,2023,37(12):76-83 |
| 接受-拒绝算法的贝叶斯不确定度评定 |
| Bayesian uncertainty evaluation based on accept-reject algorithm |
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| DOI： |
| 中文关键词: 测量不确定度 贝叶斯统计 接受-拒绝采样 蒙特卡洛方法 |
| 英文关键词:measurement uncertainty Bayesian statistics accept-reject sampling Monte Carlo method |
| 基金项目:国家重点研发计划项目(2019YFB2004901)资助 |
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| Author | Institution |
| Li Hongli | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
| Chen Zhiwen | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
| Zhang Zuyang | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
| Zhao Zhihao | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
| Huang Qiangxian | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
| Zhang Liansheng | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
| Cheng Rongjun | 1. School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology,2. Anhui Province Key Laboratory of Measuring Theory and Precision Instrument,
Hefei University of Technology |
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| 中文摘要: |
| 针对贝叶斯不确定度评定中获取测量模型后验分布困难的问题,给出一种基于接受-拒绝采样思想实现贝叶斯测量不
确定度评定的方法。 面向线性/ 非线性测量模型,先利用贝叶斯假设或蒙特卡洛法获得被测量的先验信息,再基于接受-拒绝采
样获得被测量的接受采样点形成后验分布,对被测量进行统计推断得到测量不确定度评定结果。 通过规范示例和实际测量评
定实例,验证了采用接受-拒绝算法的贝叶斯不确定度评定方法相较于传统 GUM 和 MCM 评定方法,能够得到可靠评定结果,且
获取贝叶斯后验分布过程简便,在无/ 有历史信息条件下测量不确定度评定应用中具有可行性和实用性。 |
| 英文摘要: |
| Aiming at the difficulty of obtaining the posterior distribution of measurement model in Bayesian uncertainty evaluation, a
method based on accept-reject sampling is proposed to realize Bayesian measurement uncertainty evaluation. For linear/ nonlinear
measurement model, the prior information being measured is obtained by using Bayesian hypothesis or Monte Carlo method, the accepted
sampling points being measured are obtained based on accept-reject sampling. Then the posterior distribution is formed based on these
accepted sampling points, and the measurement uncertainty evaluation results are obtained by statistical inference. Through the two
evaluation examples which come from the specification and practical measurement application, it is verified that the Bayesian uncertainty
evaluation method using the accept-reject algorithm can obtain reliable evaluation results compared with traditional GUM and MCM
methods, the process of obtaining the Bayesian posterior distribution is simple, and it is feasible and practical in the application of
measurement uncertainty evaluation under the condition of without / with historical information. |
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