Bayesian Monte Carlo method for monotonic models applying the Generalized Beta distribution

2011 ◽  
Vol 18 (4) ◽  
pp. 1153-1161 ◽  
Author(s):  
M. Rajabalinejad ◽  
Z. Demirbilek
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Beta Distribution ◽  
Bayesian Monte Carlo ◽  
Generalized Beta Distribution

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

scholarly journals Predicting Battery Aging Trajectory via a Migrated Aging Model and Bayesian Monte Carlo Method

2019 ◽  
Vol 158 ◽  
pp. 2456-2461 ◽  
Author(s):  
Xiaopeng Tang ◽  
Ke Yao ◽  
Changfu Zou ◽  
Boyang Liu ◽  
Furong Gao
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Aging Model ◽  
Bayesian Monte Carlo

scholarly journals Fission yield covariances for JEFF: A Bayesian Monte Carlo method

2017 ◽  
Vol 146 ◽  
pp. 09023 ◽  
Author(s):  
Olivier Leray ◽  
Dimitri Rochman ◽  
Michael Fleming ◽  
Jean-Christophe Sublet ◽  
Arjan Koning ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Fission Yield ◽  
Bayesian Monte Carlo

scholarly journals Monte Carlo integral adjustment of nuclear data libraries – experimental covariances and inconsistent data

2019 ◽  
Vol 211 ◽  
pp. 07007
Author(s):  
Henrik Sjöstrand ◽  
Georg Schnabel
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Marginal Likelihood ◽  
Nuclear Data ◽  
Model Calculations ◽  
Adjustment Procedure ◽  
Inconsistent Data ◽  
Bayesian Monte Carlo ◽  
Data Libraries ◽  
Nuclear Data Libraries

Integral experiments can be used to adjust nuclear data libraries. Here a Bayesian Monte Carlo method based on assigning weights to the different random files is used. If the experiments are inconsistent within them-self or with the nuclear data it is shown that the adjustment procedure can lead to undesirable results. Therefore, a technique to treat inconsistent data is presented. The technique is based on the optimization of the marginal likelihood which is approximated by a sample of model calculations. The sources to the inconsistencies are discussed and the importance to consider correlation between the different experiments is emphasized. It is found that the technique can address inconsistencies in a desirable way.

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