Composite Gaussian process models for emulating expensive functions

DOI10.1214/12-AOAS570zbMath1257.62089arXiv1301.2503OpenAlexW2027973545MaRDI QIDQ1940022

Shan Ba, V. Roshan Joseph

Publication date: 5 March 2013

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.2503

zbMATH Keywords

computer experimentsKrigingfunctional approximationnonstationary Gaussian processnuggets

Mathematics Subject Classification ID

Related Items (22)

Prediction of non-stationary response functions using a Bayesian composite Gaussian process ⋮ A Framework for Controlling Sources of Inaccuracy in Gaussian Process Emulation of Deterministic Computer Experiments ⋮ Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available ⋮ Designing combined physical and computer experiments to maximize prediction accuracy ⋮ Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations ⋮ Combined Global and Local Search for Optimization with Gaussian Process Models ⋮ Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models ⋮ A Clustered Gaussian Process Model for Computer Experiments ⋮ Predictive Subdata Selection for Computer Models ⋮ Composite T-process regression models ⋮ Calibrating a large computer experiment simulating radiative shock hydrodynamics ⋮ Multi-fidelity cost-aware Bayesian optimization ⋮ Computer experiment designs for accurate prediction ⋮ Analysis methods for computer experiments: how to assess and what counts? ⋮ Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments ⋮ Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures ⋮ Regression analysis for multivariate process data of counts using convolved Gaussian processes ⋮ Gaussian Process Modeling of Finite Element Models with Functional Inputs ⋮ On the inference of applying Gaussian process modeling to a deterministic function ⋮ Analyzing stochastic computer models: a review with opportunities ⋮ Effective model calibration via sensible variable identification and adjustment with application to composite fuselage simulation ⋮ Multi-scale Vecchia approximations of Gaussian processes

Uses Software

• spBayes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Accurate emulators for large-scale computer experiments
• Cases for the nugget in modeling computer experiments
• The design and analysis of computer experiments.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Case studies in Bayesian statistics. Vol 6: Proceedings of the 6th workshop, Carnegie Mellon University, Pittsburgh, PA, USA, September 28--29, 2001
• Estimating deformations of isotropic Gaussian random fields on the plane
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
• Stochastic Kriging for Simulation Metamodeling
• Multi-Layer Designs for Computer Experiments
• A non-stationary covariance-based Kriging method for metamodelling in engineering design
• Bayesian Inference for Non-Stationary Spatial Covariance Structure via Spatial Deformations
• Strictly Proper Scoring Rules, Prediction, and Estimation
• A class of stochastic volatility models for environmental applications
• On the condition number of covariance matrices in kriging, estimation, and simulation of random fields
• An alternative measure of the reliability of ordinary kriging estimates

This page was built for publication: Composite Gaussian process models for emulating expensive functions