Prediction of non-stationary response functions using a Bayesian composite Gaussian process
From MaRDI portal
Publication:829711
DOI10.1016/j.csda.2020.107083OpenAlexW3082988878MaRDI QIDQ829711
Christopher M. Hans, Casey B. Davis, Thomas J. Santner
Publication date: 6 May 2021
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2020.107083
emulatoruncertainty quantificationuniversal krigingcomposite Gaussian process modelGaussian process interpolatortreed Gaussian process model
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Cases for the nugget in modeling computer experiments
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Optimal scaling for various Metropolis-Hastings algorithms.
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- The design and analysis of computer experiments
- Composite Gaussian process models for emulating expensive functions
- Robust Gaussian stochastic process emulation
- Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
- Stochastic Kriging for Simulation Metamodeling
- Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations
- A non-stationary covariance-based Kriging method for metamodelling in engineering design
- Strictly Proper Scoring Rules, Prediction, and Estimation