Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments
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Summary
Some Bayesian hierarchical Gaussian process models are proposed, which tend to produce prediction closer to that from the high-accuracy experiment.
- Type
- article
- Published
- 2008-05-01
- Cited by
- 391
- References
- 37
- OpenAlex
- https://openalex.org/W1991413021
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:9105252
Keywords
Computer science, Markov chain Monte Carlo, Bayesian probability, Computation, Monte Carlo method
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- Computer model validation with functional output
- Modeling the steady-state thermodynamic operation point of top-spray fluidized bed processing
- The Identification of Structural Characteristics
- Convergence theorems for a class of simulated annealing algorithms on ℝ d
- Identification in Parametric Models
- On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs
- A Bayesian analysis of kriging
- Monte Carlo strategies in scientific computing
- Integrated Analysis of Computer and Physical Experiments
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- Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement
- Statistical Adjustments to Engineering Models
- Prediction and Calibration Using Outputs from Multiple Computer Simulators
- High Definition Metrology based Process Control: Measurement System Analysis and Process Monitoring.
- Construction of some new families of nested orthogonal arrays
- Computer experiments with both quantitative and qualitative inputs
- Bayesian Modeling and Optimization of Functional Responses Affected by Noise Factors
- Nested Lattice Sampling: A New Sampling Scheme Derived by Randomizing Nested Orthogonal Arrays
- A Review of Statistical Methods for Quality Improvement and Control in Nanotechnology
- Multi-fidelity Gaussian process regression for computer experiments
- Statistical adjustment, calibration, and uncertainty quantification of complex computer models
- Statistical Metamodeling and Sequential Design of Computer Experiments to Model Glyco-Altered Gating of Sodium Channels in Cardiac Myocytes
- Designing simulation experiments with controllable and uncontrollable factors for applications in healthcare
- Planification d'expériences séquentielle dans un contexte de méta-modélisation multi-fidélité.
- Combining adaptive and designed statistical experimentation : process improvement, data classification, experimental optimization and model building
- Adapted reservoir characterization for monitoring and uncertainty analysis of CO2 storage
- Model Migration through Bayesian Adjustments
- Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes
- Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements
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