Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Keyphrases

• Gaussian Process 100%
• Inverse Uncertainty Quantification 100%
• Modular Bayesian Approach 100%
• Computer Model 50%
• Model Mismatch 33%
• Input Uncertainty 33%
• Underground Physics 16%
• Nuclear Reactor 16%
• Reactor Systems 16%
• Validation Studies 16%
• Approximation Error 16%
• Sensitivity Study 16%
• Bayesian Analysis 16%
• Overfitting 16%
• Numerical Approximation 16%
• Expert Opinion 16%
• Model Updating 16%
• Model Output 16%
• Uncertainty Study 16%
• Acceptance Criteria 16%
• Update Equation 16%
• Output Uncertainty 16%
• Self-evaluation 16%
• Information Uncertainty 16%
• Uncertainty Sensitivity 16%
• Metamodel 16%
• System Design Analysis 16%
• Best Estimate Plus Uncertainty 16%
• System Safety Analysis 16%
• Uncertainty Validation 16%
• Model Input Uncertainty 16%
• Full Bayesian Approach 16%

Engineering

• Gaussians 100%
• Uncertainty Quantification 100%
• Bayesian Approach 100%
• Input Uncertainty 50%
• Limitations 16%
• Nuclear Reactor 16%
• Reactor System 16%
• Specifies 16%
• Design Analysis 16%
• Safety Analysis 16%
• Expert Opinion 16%
• Model Input 16%
• Acceptance Criterion 16%