Using Bayesian Statistics to Model Uncertainty in Mixture Models: A Sensitivity Analysis of Priors

2016 ◽  
Vol 24 (2) ◽  
pp. 198-215 ◽  
Author(s):  
Sarah Depaoli ◽  
Yuzhu Yang ◽  
John Felt
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Statistics ◽  
Mixture Models ◽  
Model Uncertainty

scholarly journals Basic Framework and Main Methods of Uncertainty Quantification

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Juan Zhang ◽  
Junping Yin ◽  
Ruili Wang
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Inference ◽  
Uncertainty Quantification ◽  
Model Uncertainty ◽  
Model Calibration ◽  
Uncertainty Propagation ◽  
Propagation Model ◽  
Practical Significance ◽  
The Core ◽  
Basic Framework

Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.

Sign in / Sign up

Export Citation Format

Share Document