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Ensemble Bayesian method for parameter distribution inference:application to reactor physics 被引量:2
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作者 Jia‑Qin Zeng Hai‑Xiang Zhang +1 位作者 He‑Lin Gong Ying‑Ting Luo 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第12期216-228,共13页
The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model ... The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering. 展开更多
关键词 Model parameters bayesian inference Frequency distribution ensemble bayesian method KL divergence
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