scholarly journals Bayesian Monte Carlo extraction of the sea asymmetry with SeaQuest and STAR data

2021 ◽  
Vol 104 (7) ◽  
Author(s):  
C. Cocuzza ◽  
W. Melnitchouk ◽  
A. Metz ◽  
N. Sato ◽  
Keyword(s):  
Monte Carlo ◽  
Star Data ◽  
Bayesian Monte Carlo

scholarly journals Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 258
Author(s):  
Zhihang Xu ◽  
Qifeng Liao
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Information Gain ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Likelihood Functions ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Bayesian Monte Carlo ◽  
Expected Information Gain

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.

scholarly journals Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

2019 ◽  
Vol 254 ◽  
pp. 113591 ◽  
Author(s):  
Xiaopeng Tang ◽  
Changfu Zou ◽  
Ke Yao ◽  
Jingyi Lu ◽  
Yongxiao Xia ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Trajectory Prediction ◽  
Model Migration ◽  
Bayesian Monte Carlo
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